From adc4b2be25ae71dea6257b32e9b48dc333d05a5f Mon Sep 17 00:00:00 2001 From: Zhongwei Li Date: Sun, 30 Nov 2025 08:49:50 +0800 Subject: [PATCH] Initial commit --- .claude-plugin/plugin.json | 11 + README.md | 3 + plugin.lock.json | 617 +++++++++ skills/mxcp-expert/README.md | 273 ++++ skills/mxcp-expert/SKILL.md | 1059 ++++++++++++++++ .../project-templates/confluence/README.md | 153 +++ .../project-templates/confluence/config.yml | 12 + .../confluence/mxcp-site.yml | 7 + .../mxcp_plugin_confluence/__init__.py | 172 +++ .../confluence/sql/cql_query.sql | 2 + .../confluence/sql/describe_page.sql | 2 + .../confluence/sql/get_children.sql | 2 + .../confluence/sql/get_page.sql | 2 + .../confluence/sql/list_spaces.sql | 2 + .../confluence/sql/search_pages.sql | 2 + .../confluence/tools/cql_query.yml | 66 + .../confluence/tools/describe_page.yml | 28 + .../confluence/tools/get_children.yml | 28 + .../confluence/tools/get_page.yml | 28 + .../confluence/tools/list_spaces.yml | 21 + 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skill provides complete guidance for: + +- **Creating MXCP projects** - Initialize and structure production-ready MCP servers +- **Endpoint development** - Build tools, resources, and prompts using SQL or Python +- **Enterprise features** - Implement authentication, policies, and audit logging +- **dbt integration** - Combine data transformation with MCP endpoints +- **Quality assurance** - Validate, test, lint, and evaluate your endpoints +- **Production deployment** - Monitor drift, track operations, and ensure security + +## Installation + +1. Download the `mxcp-expert.zip` file +2. In Claude Desktop, go to Settings → Developer → Custom Skills +3. Click "Add Skill" and upload the `mxcp-expert.zip` file +4. The skill will be automatically available when working on MXCP projects + +## Skill Structure + +``` +mxcp-expert/ +├── SKILL.md # Main skill file with quick reference +├── assets/ # Project templates and resources +│ ├── project-templates/ # Pre-built MXCP projects +│ │ ├── google-calendar/ # OAuth integration examples +│ │ ├── jira/ jira-oauth/ # Jira integrations +│ │ ├── salesforce/ salesforce-oauth/ # Salesforce integrations +│ │ ├── confluence/ # Confluence integration +│ │ ├── python-demo/ # Python endpoint patterns +│ │ ├── covid_owid/ # dbt data caching example +│ │ ├── keycloak/ # SSO integration +│ │ └── ... # More templates +│ └── schemas/ # JSON Schema definitions for YAML validation +│ ├── mxcp-site-schema-1.json # mxcp-site.yml validation +│ ├── mxcp-config-schema-1.json # config.yml validation +│ ├── tool-schema-1.json # Tool definition validation +│ ├── resource-schema-1.json # Resource definition validation +│ ├── prompt-schema-1.json # Prompt definition validation +│ ├── eval-schema-1.json # Evaluation suite validation +│ └── common-types-schema-1.json # Common type definitions +├── scripts/ # Utility scripts +│ └── validate_yaml.py # YAML validation script +└── references/ # Detailed documentation (23 files) + ├── tool-templates.md # Ready-to-use YAML templates for tools/resources/prompts + ├── project-selection-guide.md # Decision tree and template selection + ├── dbt-core-guide.md # Essential dbt knowledge (seeds, models, Python models) + ├── duckdb-essentials.md # DuckDB features and SQL extensions + ├── endpoint-patterns.md # Complete endpoint examples + ├── python-api.md # Python runtime API reference + ├── policies.md # Policy enforcement guide + ├── comprehensive-testing-guide.md # Complete testing strategies + ├── debugging-guide.md # Systematic debugging workflows + ├── error-handling-guide.md # Python error handling patterns + ├── dbt-patterns.md # Advanced dbt integration patterns + ├── database-connections.md # PostgreSQL, MySQL, SQLite, SQL Server + ├── excel-integration.md # Excel file handling with pandas and dbt + ├── claude-desktop.md # Claude Desktop setup guide + ├── cli-reference.md # Complete CLI documentation + ├── type-system.md # Type validation reference + └── ... # 8 more specialized guides +``` + +## When This Skill is Used + +Claude will automatically use this skill for ANY mention or reference to MXCP, including when you: + +- Mention "MXCP", "mxcp", "MXCP server", "MXCP project" in any context +- Ask questions about MXCP or its features +- Create, initialize, build, or set up MXCP projects +- Edit or modify MXCP files (mxcp-site.yml, config.yml, tools/resources/prompts) +- Define or work with MCP tools, resources, or prompts using MXCP +- Implement SQL queries or Python endpoints for MXCP +- Configure MXCP features (authentication, policies, audit logging, dbt integration) +- Run MXCP commands (mxcp validate, mxcp test, mxcp serve, mxcp init, mxcp lint) +- Test, validate, lint, or debug MXCP endpoints +- Fix MXCP validation errors or troubleshoot issues +- Deploy MXCP servers to production + +## Quick Start Examples + +### Example 1: CSV File to MCP Server + +``` +User: "I need to connect my chat to a CSV file with customer data" + +Claude: [Uses mxcp-expert skill] +I'll help you create an MXCP server that queries your CSV file. + +1. Initialize project: + mkdir customer-mcp && cd customer-mcp + mxcp init --bootstrap + +2. Add your CSV as a dbt seed: + cp customers.csv seeds/ + +3. Create schema definition (seeds/schema.yml): + [Creates proper schema.yml with column types and tests] + +4. Load data: + dbt seed + dbt test + +5. Create query tool: + [Creates tools/get_customers.yml with parameterized SQL] + +6. Test and validate: + mxcp validate && mxcp test + mxcp serve + +[Provides complete security-first implementation with validation] +``` + +### Example 2: API Integration + +``` +User: "Connect to Google Calendar API" + +Claude: [Uses mxcp-expert skill] +I'll use the google-calendar project template for OAuth integration. + +1. Copy template: + cp -r assets/project-templates/google-calendar calendar-mcp + cd calendar-mcp + +2. Configure OAuth in project config.yml: + [Creates config.yml with OAuth settings in project directory] + +3. Test authentication: + mxcp serve + [Explains OAuth flow] + +[Provides complete working OAuth integration] +``` + +### Example 3: Database Connection + +``` +User: "Connect to my PostgreSQL production database" + +Claude: [Uses mxcp-expert skill] +I'll help you connect to your PostgreSQL database using DuckDB ATTACH. + +1. Create project and config: + mkdir postgres-mcp && cd postgres-mcp + mxcp init --bootstrap + + Create config.yml with database credentials (uses environment variables) + +2. Create SQL tool with ATTACH: + [Creates tools/query_customers.yml with PostgreSQL ATTACH statement] + +3. Set credentials and test: + export DB_HOST="localhost" DB_USER="readonly_user" DB_PASSWORD="xxx" + mxcp validate && mxcp run tool query_customers + +4. Alternative: Cache data with dbt for fast queries + [Shows dbt source + model pattern to materialize data] + +[Provides both direct query and cached approaches with security best practices] +``` + +## Key Features Covered + +### Endpoint Development +- SQL tools for data queries +- Python tools for complex logic +- Resources for data access +- Prompts with Jinja templates +- Combined SQL + Python patterns + +### Enterprise Features +- OAuth authentication (GitHub, Google, Microsoft, etc.) +- Policy-based access control with CEL expressions +- Comprehensive audit logging (JSONL format) +- Field-level data filtering and masking +- User context testing + +### Quality Assurance +- Structure validation with `mxcp validate` +- Functional testing with `mxcp test` +- Metadata quality checks with `mxcp lint` +- LLM behavior testing with `mxcp evals` + +### dbt Integration +- Data transformation pipelines +- External data caching +- Incremental model patterns +- Data quality tests + +### Production Operations +- Drift detection and monitoring +- Audit log querying and export +- Multi-environment profiles +- Secrets management (Vault, 1Password) +- OpenTelemetry observability + +## Core Principles + +This skill prioritizes: + +1. **Security First** - Authentication, authorization, parameterized queries, input validation +2. **Robustness** - Error handling, type safety, data quality checks +3. **Validity** - Schema compliance, structure validation +4. **Testability** - Comprehensive test coverage +5. **Testing** - Always validate/test/lint before deployment + +## Mandatory Workflow + +**To ensure MXCP servers always work correctly, the agent follows:** + +1. **Build incrementally** - One tool/component at a time +2. **Validate continuously** - `mxcp validate` after each change +3. **Test before proceeding** - `mxcp test` must pass before next step +4. **Verify manually** - Run actual tool with real data +5. **Definition of Done** - ALL validation checks must pass + +The agent will NEVER declare a project complete unless all validation, tests, and manual verification succeed. + +## Documentation Coverage + +The skill includes comprehensive documentation based on official MXCP docs: + +**CRITICAL - Start Here**: +- **build-and-validate-workflow.md** - MANDATORY workflow ensuring correctness +- **minimal-working-examples.md** - Guaranteed-to-work examples (copy, test, customize) + +**Essential Guides** (for most projects): +- **project-selection-guide.md** - Decision trees, heuristics, when to use which approach +- **dbt-core-guide.md** - dbt seeds, models, schema.yml (critical for CSV/data projects) +- **duckdb-essentials.md** - DuckDB SQL features, CSV import, analytics +- **excel-integration.md** - Excel file handling and pandas integration +- **synthetic-data-patterns.md** - Generate test data with GENERATE_SERIES + +**Detailed References**: +- **endpoint-patterns.md** - Complete tool/resource/prompt examples +- **python-api.md** - Python runtime API and library wrapping patterns +- **testing-guide.md** - Comprehensive testing strategies +- **policies.md** - Policy enforcement and security +- **type-system.md** - Type validation rules +- **cli-reference.md** - Complete CLI documentation +- **claude-desktop.md** - Claude Desktop integration +- **dbt-patterns.md** - Advanced dbt integration patterns + +## About MXCP + +MXCP is an enterprise-grade MCP (Model Context Protocol) framework that provides a structured methodology for building production AI applications: + +1. **Data Quality First** - Start with dbt models and data contracts +2. **Service Design** - Define types, security policies, and API contracts +3. **Smart Implementation** - Choose SQL for data, Python for logic +4. **Quality Assurance** - Validate, test, lint, and evaluate +5. **Production Operations** - Monitor drift, track audits, ensure performance + +## License + +This skill compiles information from the MXCP project documentation. +MXCP is released under the Business Source License 1.1 (BSL). + +For more information about MXCP: +- Website: https://mxcp.dev +- GitHub: https://github.com/raw-labs/mxcp +- Contact: mxcp@raw-labs.com + +## Skill Version + +Version: 1.0.0 +Created: October 2025 +Based on: MXCP documentation as of October 2025 diff --git a/skills/mxcp-expert/SKILL.md b/skills/mxcp-expert/SKILL.md new file mode 100644 index 0000000..c7f74ce --- /dev/null +++ b/skills/mxcp-expert/SKILL.md @@ -0,0 +1,1059 @@ +--- +name: mxcp-expert +description: This skill must be used with any prompt that uses MXCP. MXCP is an enterprise-grade MCP (Model Context Protocol) framework for building production AI applications with SQL and Python endpoints, security, audit trails, policy enforcement, and comprehensive testing. Use this skill when working with MXCP in any capacity including creating, building, initializing, setting up, or editing MXCP servers or projects, configuring MCP tools/resources/prompts, implementing endpoints, setting up authentication/policies, debugging validation errors, or troubleshooting MXCP applications. +--- + +# MXCP: Enterprise MCP Framework + +MXCP is an enterprise-grade MCP (Model Context Protocol) framework for building production AI applications with SQL and Python. This skill provides comprehensive guidance for working with MXCP projects. + +**This skill supports both creating new and editing existing MXCP projects.** Use this skill whether you're starting a new project from scratch or modifying an existing MXCP server (adding/removing tools, updating configurations, changing implementations, fixing validation errors, etc.). + +## Scope: Technical Implementation Only + +This skill focuses on **how to implement** MCP servers using MXCP, not **what to implement**. + +**In Scope**: +- Choosing technical approaches (SQL vs Python, OAuth vs token auth) +- Implementing endpoints, authentication, policies +- Testing, validation, debugging +- Security and robustness patterns + +**Out of Scope**: +- Defining business requirements or use cases +- Determining what features the MCP server should provide +- Business logic design decisions + +**When user needs are unclear technically**: Ask clarifying questions about data sources, authentication, access patterns. + +**When user needs are unclear functionally**: Ask the user to clarify their business requirements before proceeding. + +## Quick Reference + +**When to use this skill:** +- Creating or initializing new MXCP projects +- Editing existing MXCP projects (adding, removing, or modifying any components) +- Defining or modifying MCP tools, resources, or prompts +- Implementing or updating SQL queries or Python endpoints +- Configuring or changing authentication, policies, or audit logging +- Setting up or modifying dbt integration for data transformation +- Testing, validating, or debugging MXCP endpoints +- Fixing validation errors from `mxcp validate`, `mxcp test`, or `mxcp lint` +- Refactoring or restructuring MXCP project files +- Deploying MXCP servers to production + +**New to MXCP? Quick navigation:** +- **First time?** → See [Getting Started](#getting-started) for project initialization +- **Learning by example?** → See [Project Templates](#working-with-project-templates) (14 complete examples) +- **Building Python tools?** → Copy `assets/project-templates/python-demo/` +- **Working with CSV data?** → Copy `assets/project-templates/covid_owid/` +- **Need OAuth integration?** → Copy `assets/project-templates/google-calendar/` +- **Stuck on an error?** → See **references/debugging-guide.md** +- **Need YAML validation?** → Use `python scripts/validate_yaml.py` (see [YAML Schema Validation](#yaml-schema-validation)) + +## ⚠️ COMMON MISTAKES TO AVOID + +**READ THIS BEFORE CREATING ANY TOOLS** - These mistakes cause validation errors: + +### 1. Wrong Tool Definition Structure + +❌ **WRONG** (Missing `tool:` wrapper): +```yaml +mxcp: 1 +name: get_calendar +description: ... +language: python +``` + +✅ **CORRECT**: +```yaml +mxcp: 1 +tool: + name: get_calendar + description: ... + language: python +``` + +**Fix**: Always add `tool:` as a top-level key after `mxcp: 1`. + +### 2. Using `type:` Instead of `language:` for Python Tools + +❌ **WRONG**: +```yaml +tool: + name: my_tool + type: python # ❌ Wrong field name +``` + +✅ **CORRECT**: +```yaml +tool: + name: my_tool + language: python # ✅ Correct for Python tools +``` + +**Fix**: Use `language: python` for Python tools. Use `type: sql` for SQL tools. + +### 3. Misusing the `required:` Field + +❌ **WRONG** (Will cause validation error): +```yaml +parameters: + - name: ticker + type: string + required: true # ❌ Causes: "True is not of type 'array'" +``` + +❌ **ALSO WRONG**: +```yaml +parameters: + - name: city + type: string + required: false # ❌ Not valid syntax +``` + +✅ **CORRECT** (Required parameter): +```yaml +parameters: + - name: ticker + type: string + description: "Stock ticker symbol" + # No default = required by default +``` + +✅ **CORRECT** (Optional parameter): +```yaml +parameters: + - name: city + type: string + description: "Filter by city (optional)" + default: null # Makes it optional +``` + +✅ **CORRECT** (Optional with specific default): +```yaml +parameters: + - name: limit + type: integer + description: "Maximum results" + default: 100 # Optional, defaults to 100 +``` + +**Fix**: +- For required parameters: Don't add `required:` field at all +- For optional parameters: Add `default: null` or `default: ` + +### 4. Not Validating Early Enough + +❌ **WRONG** (Creating multiple tools before validating): +```bash +# Create tool1.yml +# Create tool2.yml +# Create tool3.yml +# Create tool4.yml +mxcp validate # ❌ Now you have errors in 4 files! +``` + +✅ **CORRECT** (Validate after EACH tool): +```bash +# Create tool1.yml +mxcp validate # ✅ Fix errors NOW +# Create tool2.yml +mxcp validate # ✅ Fix errors NOW +# Continue... +``` + +**Fix**: Run `mxcp validate` immediately after creating EACH tool definition. + +### 5. Not Reading Examples First + +❌ **WRONG**: Creating YAML from scratch based on assumptions. + +✅ **CORRECT**: +1. Read **references/minimal-working-examples.md** FIRST +2. Copy a working example +3. Modify incrementally +4. Validate after each change + +## Before You Start: Mandatory Checklist + +**Before creating ANY tool, complete this checklist in order:** + +- [ ] 1. Read **references/minimal-working-examples.md** to see working examples +- [ ] 2. Identify which example is closest to the use case +- [ ] 3. Copy the relevant example as a starting point +- [ ] 4. Review the tool template below +- [ ] 5. Modify the copied example incrementally +- [ ] 6. Validate after EACH change + +**DO NOT skip this checklist. DO NOT create YAML from scratch.** + +## Quick Start: Tool Templates + +**Copy ready-to-use templates to avoid syntax errors:** + +- **Python Tool Template** - For custom logic, API calls, complex processing +- **SQL Tool Template** - For database queries and data retrieval +- **Resource Template** - For static or dynamic data resources +- **Prompt Template** - For LLM instruction prompts + +**See references/tool-templates.md** for complete templates with examples. + +**Quick template workflow**: +1. Copy appropriate template from references/tool-templates.md +2. Replace `YOUR_TOOL_NAME` with actual name +3. Update description, parameters, and return types +4. 🛑 **RUN `mxcp validate` IMMEDIATELY** 🛑 + +## Core Principles + +**ALWAYS prioritize in this order:** + +1. **Security** - Authentication, authorization, input validation, parameterized queries +2. **Robustness** - Error handling, type validation, data quality checks +3. **Validity** - Structure validation, schema compliance, type safety +4. **Testability** - Test cases, validation scripts, lint checks +5. **Testing** - Run validate/test/lint before deployment +6. **Features** - Implement requested functionality based on user needs + +## Mandatory Build Workflow + +**CRITICAL: Follow this exact workflow to ensure correctness** + +🚨 **DO NOT create multiple tools before validating the first one!** 🚨 + +### Step-by-Step Process + +1. **Create ONE tool definition YAML** (e.g., `tools/my_tool.yml`) +2. 🛑 **STOP! Run `mxcp validate` RIGHT NOW** 🛑 +3. **Fix ALL validation errors before proceeding** +4. **Create Python implementation** (if needed, e.g., `python/my_service.py`) +5. 🛑 **STOP! Run `mxcp validate` AGAIN** 🛑 +6. **Add tests to the tool YAML** (in `tests:` section) +7. **Run `mxcp test` to verify functionality** +8. **Manual verification**: `mxcp run tool ` +9. **Only after ALL checks pass**, create the next tool + +### Correct Workflow Example + +```bash +# Create first tool +cat > tools/tool1.yml < python/service1.py < tools/tool2.yml <` + 5. Add parameters for filtering/pagination + 6. Test with `dbt test` and `mxcp test` + +**API Integration → MCP Server**: +- **Templates**: + - OAuth: `google-calendar/`, `jira-oauth/`, `salesforce-oauth/` + - Token: `jira/`, `salesforce/`, `confluence/` + - SSO: `keycloak/` +- **Steps**: + 1. Check `assets/project-templates/` for matching template + 2. If found: copy template, adapt configuration + 3. If not found: use `python-demo/` template as base + 4. Implement authentication (OAuth/token) + 5. Create Python tools for API operations + 6. Add error handling and retries + +**Python Tools → MCP Server**: +- **Template**: `assets/project-templates/python-demo/` (START HERE) +- **Steps**: + 1. Copy python-demo template + 2. Review example tools: `analyze_numbers`, `create_sample_data`, `process_time_series` + 3. Adapt Python functions in `python/` directory + 4. Update tool definitions in `tools/` + 5. Follow Python development workflow (black → pyright → pytest) + +**Database → MCP Server**: +- **Approach 1 - Direct Query** (real-time data): + 1. Use DuckDB `ATTACH` with PostgreSQL, MySQL, SQLite, SQL Server + 2. Create SQL tools with `ATTACH IF NOT EXISTS` in tool definition + 3. Store credentials in environment variables (config.yml) + 4. Use read-only database users for security + 5. Add parameterized queries (`$param`) to prevent SQL injection + +- **Approach 2 - Cached Data** (fast queries, dbt): + 1. Define external database as dbt source + 2. Create dbt model to materialize/cache data in DuckDB + 3. Run `dbt run` to fetch and cache data + 4. Run `dbt test` for data quality validation + 5. Create MXCP tools to query cached data (very fast) + 6. Create refresh tool to update cache periodically + +- **Examples**: + - **minimal-working-examples.md** - Example 6 (PostgreSQL direct), Example 7 (dbt cache) + - **references/database-connections.md** - Complete guide with all databases + +**See**: +- [Project Templates](#working-with-project-templates) for all 14 templates +- **references/project-selection-guide.md** for complete decision tree +- **references/database-connections.md** for database connection patterns + +## Getting Started + +### Initialize a New Project + +**CRITICAL: Always use `uv` for Python environment management.** + +**IMPORTANT: Project directory location**: +- If the user specifies a project name or wants a new directory, create a new directory +- If the user is already in an empty directory or wants to initialize in the current location, use the current working directory +- When in doubt, ask the user whether to create a new directory or use the current directory + +```bash +# Option A: Create new project in a new directory (if user specified a project name) +mkdir my-mxcp-project && cd my-mxcp-project + +# Option B: Use current working directory (if already in desired location) +# Skip the mkdir and cd commands, proceed directly to step 2 + +# 2. Create virtual environment with uv +uv venv + +# 3. Activate virtual environment +source .venv/bin/activate # On Unix/macOS +# OR +.venv\Scripts\activate # On Windows + +# 4. Install MXCP and development tools +uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov + +# 5. Initialize MXCP project +mxcp init --bootstrap + +# This creates: +# - mxcp-site.yml with default config +# - Organized directory structure +# - Example hello-world endpoints (SQL + Python) +# - server_config.json for Claude Desktop + +# 6. Clean up example files (recommended) +# The bootstrap creates hello-world examples for learning, but should be removed for production projects +rm tools/hello_world.yml +rm sql/hello_world.sql +``` + +### Python Development Workflow + +**ALWAYS follow this workflow when working with Python code:** + +```bash +# 1. Activate virtual environment (if not already active) +source .venv/bin/activate + +# 2. After creating/editing Python files, format with black +black python/ + +# 3. Run type checker +pyright python/ + +# 4. Run unit tests +pytest tests/ -v + +# 5. Only after all checks pass, proceed +``` + +**Mandatory tooling**: +- **uv**: Virtual environment and package management +- **black**: Code formatting (run after every edit) +- **pyright**: Type checking (run after every edit) +- **pytest**: Unit testing with coverage + +### Start the Server + +**ALWAYS activate virtual environment before running MXCP commands:** + +```bash +# Activate environment first +source .venv/bin/activate + +# Start with stdio transport (for Claude Desktop) +mxcp serve + +# Start with HTTP transport +mxcp serve --transport http --port 8080 + +# Use specific profile +mxcp serve --profile production +``` + +## Working with Project Templates + +MXCP provides 14 complete, runnable project templates in `assets/project-templates/` for common integration scenarios. Each template includes complete tool definitions, Python implementations, configuration examples, and comprehensive READMEs. + +### Available Templates + +**Start here:** +- **`python-demo`** - Python endpoint patterns (START HERE for Python tools) +- **`covid_owid`** - CSV + dbt workflow (START HERE for data projects) + +**By use case:** +| Category | Templates | +|----------|-----------| +| Data | `covid_owid`, `earthquakes` | +| OAuth APIs | `google-calendar`, `jira-oauth`, `salesforce-oauth` | +| Token APIs | `jira`, `salesforce`, `confluence` | +| Enterprise Auth | `keycloak` | +| Advanced | `plugin` (DuckDB plugins) | + +### Using Templates + +```bash +cp -r assets/project-templates/google-calendar my-project +cd my-project +uv venv && source .venv/bin/activate +uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov +cat README.md # Follow template-specific setup +mxcp validate && mxcp test && mxcp serve +``` + +See **references/project-selection-guide.md** for detailed template selection guidance. + +## Creating Endpoints + +**Two types of tools:** + +1. **Custom SQL/Python Tools** - Defined in `tools/*.yml` for specific use cases +2. **Generic SQL Tools** - Built-in tools (`list_tables`, `get_table_schema`, `execute_sql_query`) that allow LLMs to explore and query databases dynamically + +**Enable generic SQL tools** for natural language data exploration: +```yaml +# mxcp-site.yml +sql_tools: + enabled: true +``` + +### Understanding Generic SQL Tools + +**When They Are Available**: +- ✅ **Runtime only** - Available when MCP server is running (via `mxcp serve` or during `mxcp evals`) +- ✅ **Can be tested with `mxcp evals`** - Evals automatically start an internal server +- ❌ **Cannot be tested with `mxcp run tool `** - They don't exist as static tool definitions in `tools/` directory +- ❌ **Cannot be tested with `mxcp test`** - These are for static tool definitions only + +**How LLMs Choose Between Generic vs Custom Tools**: + +LLMs often **prefer generic SQL tools** (`execute_sql_query`) over custom tools because: +- Generic tools offer more flexibility (arbitrary SQL queries) +- LLMs can construct queries tailored to the specific question +- LLMs don't need to find the "right" custom tool + +**When to use generic SQL tools**: +- Exploratory data analysis scenarios +- When users ask unpredictable questions +- When building a general-purpose data assistant +- For prototyping before creating custom tools + +**When to disable generic SQL tools**: +- When you want LLMs to use specific custom tools +- For production systems with strict query control +- When custom tools provide better documentation/safety +- To enforce specific data access patterns + +**Testing generic SQL tools with evaluations**: +```bash +# mxcp evals automatically starts its own internal server +# Just run evals directly - no need to run mxcp serve first +mxcp evals data_exploration + +# Generic SQL tools will be available if sql_tools.enabled: true in mxcp-site.yml +``` + +**Note**: `mxcp evals` automatically starts an internal MCP server in the background. You do NOT need to run `mxcp serve` separately. + +**Evaluation strategy with generic SQL tools**: + +If generic SQL tools are enabled, write eval assertions that accept both approaches: + +```yaml +# Allow either custom tool OR generic SQL tool +tests: + - name: get_customer_data + prompt: "Show me customer CUST_12345" + assertions: + # Don't strictly require custom tool + # Instead, verify answer quality + answer_contains: + - "CUST_12345" + - "customer" +``` + +Or disable generic SQL tools to force custom tool usage: +```yaml +# mxcp-site.yml +sql_tools: + enabled: false # LLMs must use custom tools +``` + +See **assets/project-templates/covid_owid/** for complete example and **references/cli-reference.md** for security considerations. + +For detailed examples and patterns, see: +- **references/endpoint-patterns.md** - Tool, resource, and prompt examples +- **references/python-api.md** - Python runtime API and patterns + +## Enterprise Features + +### Policy Enforcement + +Control access and filter data using policies: + +```yaml +policies: + input: + - condition: "!('hr.read' in user.permissions)" + action: deny + reason: "Missing HR read permission" + output: + - condition: "user.role != 'hr_manager'" + action: filter_fields + fields: ["salary", "ssn"] +``` + +See **references/policies.md** for comprehensive policy examples. + +### Audit Logging + +Enable audit trails for compliance: + +```yaml +# In mxcp-site.yml +profiles: + production: + audit: + enabled: true + path: audit-logs.jsonl +``` + +Query logs: +```bash +mxcp log --since 1h --tool employee_data +mxcp log --export-duckdb audit.db +``` + +### OpenTelemetry (Distributed Tracing) + +Enable production observability with OpenTelemetry: + +```yaml +# In mxcp-site.yml +profiles: + production: + telemetry: + enabled: true + endpoint: "http://otel-collector:4318" +``` + +This provides: +- Distributed tracing across your MXCP server and dependencies +- Performance metrics and monitoring +- Integration with observability platforms (Jaeger, Grafana, etc.) + +### Authentication + +Configure OAuth providers in project-local `config.yml`: + +```yaml +# config.yml (in project directory) +mxcp: 1 + +profiles: + default: + auth: + provider: github + # OAuth credentials configured here or via environment variables + secrets: + - name: api_token + type: env + parameters: + env_var: API_TOKEN + + production: + auth: + provider: github +``` + +**Usage**: +```bash +# Option 1: Use config from project directory +mxcp serve # Automatically finds ./config.yml + +# Option 2: Specify config location +MXCP_CONFIG=./config.yml mxcp serve + +# Option 3: User can manually copy to ~/.mxcp/ if preferred +cp config.yml ~/.mxcp/ +``` + +## dbt Integration + +**dbt creates the tables → MXCP queries them** + +**Core workflow:** +1. Place CSV in `seeds/` → Create `seeds/schema.yml` → Run `dbt seed` +2. Create models in `models/` → Run `dbt run` +3. Validate with `dbt test` +4. Create MXCP tools that query the tables + +**Key concepts:** +- **Seeds** - CSV files loaded as tables +- **Models** - SQL or Python transformations +- **Schema.yml** - ALWAYS create (defines types, tests, docs) + +**Quick example (CSV → tool):** +```bash +cp data.csv seeds/ # 1. Add CSV +dbt seed && dbt test # 2. Load and validate +# 3. Create tools/query.yml with: SELECT * FROM data WHERE id = $id +``` + +See **references/dbt-core-guide.md** for complete guide including Python models, Excel processing, and schema.yml patterns. + +## Agent-Centric Design + +**Design tools that LLMs can effectively use.** Key principles: +- Build for workflows, not just data access (consolidate related operations) +- Optimize for limited context (provide `detail_level` options, human-readable identifiers) +- Design actionable error messages (include `suggestion` field) +- Use consistent naming (`get_customer_*`, `analyze_sales_*`) + +See **references/agent-centric-design.md** for complete patterns. + +## Documentation Quality + +**Tools must be self-documenting for LLMs with zero prior context.** + +Every tool needs: +- **Description**: WHAT it does, WHAT it returns, WHEN to use it +- **Parameter descriptions**: Valid values, formats, examples +- **Return type descriptions**: Describe every field + +See **references/llm-friendly-documentation.md** for examples and guidelines. + +## Error Handling + +**Python tools must return structured errors:** +```python +return {"success": False, "error": "User not found", "error_code": "NOT_FOUND"} +``` + +**SQL errors are handled automatically by MXCP.** + +See **references/error-handling-guide.md** for complete patterns. + +## Quality Assurance + +**ALWAYS run quality checks before deployment:** + +```bash +# 1. Structure validation +mxcp validate # Check YAML structure, types, required fields + +# 2. Functional testing +mxcp test # Run all test cases +mxcp test tool # Test specific tool + +# 3. Data quality (if using dbt) +dbt test # Run dbt data quality tests +dbt test --select # Test specific model + +# 4. Metadata quality +mxcp lint # Check descriptions, improve documentation + +# 5. LLM behavior testing +mxcp evals # Test how LLMs interact with tools +mxcp evals suite_name # Test specific eval suite +mxcp evals --model gpt-4o # Override default model +mxcp evals --json-output # CI/CD format +``` + +### YAML Schema Validation + +**JSON Schema Specifications for MXCP Files** + +The `assets/schemas/` directory contains JSON Schema files that define the exact structure and validation rules for all MXCP YAML files: + +- **mxcp-site-schema-1.json** - Validates `mxcp-site.yml` project configuration +- **mxcp-config-schema-1.json** - Validates `config.yml` authentication and secrets +- **tool-schema-1.json** - Validates tool definitions in `tools/*.yml` +- **resource-schema-1.json** - Validates resource definitions in `resources/*.yml` +- **prompt-schema-1.json** - Validates prompt definitions in `prompts/*.yml` +- **eval-schema-1.json** - Validates evaluation suites in `evals/*.yml` +- **common-types-schema-1.json** - Common type definitions used by other schemas + +**When to use schema validation:** + +1. **During development** - Validate YAML files as you create them to catch structure errors early +2. **Before committing** - Ensure all configuration files are valid before version control +3. **In CI/CD pipelines** - Automate validation as part of your build process +4. **When debugging** - Get detailed error messages about invalid YAML structure + +**Using the validation script:** + +```bash +# Validate a single YAML file +python scripts/validate_yaml.py path/to/file.yml + +# Validate all MXCP YAML files in project templates +python scripts/validate_yaml.py --all + +# Example output: +# ✓ assets/project-templates/jira/tools/get_issue.yml +# ✗ assets/project-templates/custom/tools/bad_tool.yml +# Error: At tool -> parameters -> 0: 'type' is a required property +``` + +**How this differs from `mxcp validate`:** + +- **Schema validation** (`scripts/validate_yaml.py`) - Checks YAML structure and syntax against JSON schemas (fast, no MXCP installation needed) +- **MXCP validation** (`mxcp validate`) - Full validation including SQL syntax, Python imports, parameter types, and business logic (requires MXCP) + +**Best practice**: Use schema validation first for quick feedback, then run `mxcp validate` for comprehensive checks. + +### Creating Effective Evaluations + +**Evaluations test whether LLMs can accomplish real tasks using your tools.** + +```bash +# Run evaluations (automatically starts internal MCP server) +mxcp evals # Run all evals +mxcp evals suite_name # Run specific suite +mxcp evals --model gpt-4o # Override model +``` + +**Quick eval file format** (`evals/my-evals.yml`): +```yaml +mxcp: 1 +suite: my_tests +tests: + - name: basic_test + prompt: "What customers are at risk?" + assertions: + must_call: + - tool: analyze_churn + answer_contains: ["risk"] +``` + +**Key considerations**: +- Evals are non-deterministic - LLMs may behave differently each run +- LLMs may prefer generic SQL tools over custom tools if `sql_tools.enabled: true` +- Use relaxed assertions (`args: {}`) over strict ones for reliability + +See **references/mxcp-evaluation-guide.md** for complete guide including model configuration, assertion types, and troubleshooting. + +**Security validation checklist:** +- [ ] All SQL queries use parameterized variables (`$param`) +- [ ] Authentication configured for all endpoints requiring it +- [ ] Policies defined for sensitive data access +- [ ] Secrets stored in Vault/1Password (never in code) +- [ ] Input validation on all parameters +- [ ] Audit logging enabled for production + +**Robustness validation checklist:** +- [ ] Error handling in Python endpoints (try/except) +- [ ] NULL handling in SQL queries +- [ ] Type validation in all tool definitions +- [ ] Return type specifications complete +- [ ] Test cases cover edge cases (empty, null, invalid) + +**Before deployment workflow:** +```bash +# Run full validation suite +mxcp validate && mxcp test && mxcp lint + +# For dbt projects, also run: +dbt test + +# Create drift baseline before first deployment +mxcp drift-snapshot + +# Enable audit logging for production +# In mxcp-site.yml profiles.production: +# audit: +# enabled: true +``` + +For comprehensive testing guidance, see **references/testing-guide.md**. + +## CLI Commands Reference + +### Core Commands +- `mxcp init [--bootstrap]` - Initialize new project +- `mxcp serve` - Start MCP server +- `mxcp list` - List all endpoints +- `mxcp run tool NAME --param key=value` - Execute endpoint + +### Quality Commands +- `mxcp validate` - Check structure +- `mxcp test` - Run tests +- `mxcp lint` - Check metadata +- `mxcp evals` - Test LLM behavior + +### Data Commands +- `mxcp query "SQL"` - Execute SQL +- `mxcp dbt run` - Run dbt +- `mxcp drift-snapshot` - Create baseline +- `mxcp drift-check` - Detect changes + +### Monitoring Commands +- `mxcp log [--since 1h]` - Query audit logs +- `mxcp log-cleanup` - Apply retention + +For complete CLI documentation, see **references/cli-reference.md**. + +## Troubleshooting + +For comprehensive debugging guidance, see **references/debugging-guide.md**. + +**Quick debug workflow:** +```bash +mxcp validate --debug # Check YAML structure +mxcp test --debug # Check logic/SQL +mxcp run tool NAME --param key=value --debug # Manual test +``` + +**Common quick fixes:** +- `required: true` error → Remove `required:` field, use `default: null` for optional params +- `tool:` not found → Add `tool:` wrapper after `mxcp: 1` +- `language:` vs `type:` → Use `language: python` for Python tools +- Type mismatch → Use `number` instead of `integer` for DuckDB numeric columns + +## Best Practices + +1. **Project Structure** - Follow organized directory layout +2. **Type Safety** - Define all parameter and return types +3. **Security** - Use Vault/1Password, never commit secrets +4. **Testing** - Write tests for all endpoints +5. **Documentation** - Add descriptions, run `mxcp lint` +6. **Performance** - Use SQL for queries, Python for logic +7. **Development Workflow**: + ```bash + mxcp validate && mxcp test && mxcp lint # Development + mxcp drift-snapshot && mxcp evals # Before deployment + mxcp drift-check && mxcp log --since 24h # Production + ``` + +## Additional Resources + +### Learn by Example (Start Here!) + +**Complete Project Templates** (14 runnable examples): +- **assets/project-templates/** - Copy, customize, and run + - `python-demo/` - Python endpoint patterns (START HERE for Python) + - `covid_owid/` - CSV + dbt workflow (START HERE for data) + - `google-calendar/` - OAuth integration example + - See [Project Templates](#working-with-project-templates) for all 14 templates + +**Minimal Working Examples**: +- **references/minimal-working-examples.md** - Guaranteed-to-work code snippets + +### Reference Files Index + +**Quick lookup by topic:** + +| Topic | Reference File | +|-------|----------------| +| Validation errors | references/debugging-guide.md | +| Testing (MXCP + pytest) | references/comprehensive-testing-guide.md | +| LLM evaluations | references/mxcp-evaluation-guide.md | +| Tool/resource examples | references/endpoint-patterns.md | +| Tool templates | references/tool-templates.md | +| Python patterns | references/python-api.md | +| dbt workflows | references/dbt-core-guide.md | +| Database connections | references/database-connections.md | +| DuckDB features | references/duckdb-essentials.md | +| Error handling | references/error-handling-guide.md | +| Project selection | references/project-selection-guide.md | +| Policies | references/policies.md | +| Type system | references/type-system.md | +| CLI commands | references/cli-reference.md | + +**Critical references (read first for new projects):** +- **references/build-and-validate-workflow.md** - Mandatory validation workflow +- **references/agent-centric-design.md** - Design tools LLMs can use effectively +- **references/minimal-working-examples.md** - Guaranteed working code snippets diff --git a/skills/mxcp-expert/assets/project-templates/confluence/README.md b/skills/mxcp-expert/assets/project-templates/confluence/README.md new file mode 100644 index 0000000..045086a --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/README.md @@ -0,0 +1,153 @@ +# MXCP Confluence Plugin Example + +This example demonstrates how to use MXCP with Confluence data. It shows how to: +- Create and use a custom MXCP plugin for Confluence integration +- Query Confluence content using SQL +- Combine Confluence data with other data sources + +## Overview + +The plugin provides several UDFs that allow you to: +- Search pages using keywords and CQL queries +- Fetch page content and metadata +- List child pages and spaces +- Navigate the Confluence content hierarchy + +## Configuration + +### 1. Creating an Atlassian API Token + +**Important:** This plugin currently only supports API tokens **without scopes**. While Atlassian has introduced scoped API tokens, there are known compatibility issues when using scoped tokens with basic authentication that this plugin relies on. + +To create an API token without scopes: + +1. **Log in to your Atlassian account** at [https://id.atlassian.com/manage-profile/security/api-tokens](https://id.atlassian.com/manage-profile/security/api-tokens) + +2. **Verify your identity** (if prompted): + - Atlassian may ask you to verify your identity before creating API tokens + - Check your email for a one-time passcode and enter it when prompted + +3. **Create the API token**: + - Click **"Create API token"** (not "Create API token with scopes") + - Enter a descriptive name for your token (e.g., "MXCP Confluence Integration") + - Select an expiration date (tokens can last from 1 day to 1 year) + - Click **"Create"** + +4. **Copy and save your token**: + - Click **"Copy to clipboard"** to copy the token + - **Important:** Save this token securely (like in a password manager) as you won't be able to view it again + - This token will be used as your "password" in the configuration below + +### 2. User Configuration + +Add the following to your MXCP user config (`~/.mxcp/config.yml`). You can use the example `config.yml` in this directory as a template: + +```yaml +mxcp: 1 + +projects: + confluence-demo: + profiles: + dev: + plugin: + config: + confluence: + url: "https://your-domain.atlassian.net/wiki" + username: "your-email@example.com" + password: "your-api-token" # Use the API token you created above +``` + +**Configuration Notes:** +- Replace `your-domain` with your actual Atlassian domain +- Replace `your-email@example.com` with the email address of your Atlassian account +- Replace `your-api-token` with the API token you created in step 1 +- The `password` field should contain your API token, not your actual Atlassian password + +### 2. Site Configuration + +Create an `mxcp-site.yml` file: + +```yaml +mxcp: 1 +project: confluence-demo +profile: dev +plugin: + - name: confluence + module: mxcp_plugin_confluence + config: confluence +``` + +## Available Tools + +### Search Pages +```sql +-- Search for pages containing specific text +SELECT search_pages_confluence($query, $limit) as result; +``` + +### Get Page +```sql +-- Fetch a page's content +SELECT get_page_confluence($page_id) as result; +``` + +### Get Children +```sql +-- List direct children of a page +SELECT get_children_confluence($page_id) as result; +``` + +### List Spaces +```sql +-- List all accessible spaces +SELECT list_spaces_confluence() as result; +``` + +### Describe Page +```sql +-- Show metadata about a page +SELECT describe_page_confluence($page_id) as result; +``` + +## Example Queries + +1. Search and analyze page content: +```sql +WITH pages AS ( + SELECT * FROM search_pages_confluence('important documentation', 50) +) +SELECT + p.title as page_title, + p.space.name as space_name, + p.version.number as version, + p.metadata.created as created_date +FROM pages p +ORDER BY p.metadata.created DESC; +``` + +## Plugin Development + +The `mxcp_plugin_confluence` directory contains a complete MXCP plugin implementation that you can use as a reference for creating your own plugins. It demonstrates: + +- Plugin class structure +- Type conversion +- UDF implementation +- Configuration handling + +## Running the Example + +1. Set the `MXCP_CONFIG` environment variable to point to your config file: + ```bash + export MXCP_CONFIG=/path/to/examples/confluence/config.yml + ``` + +2. Start the MXCP server: + ```bash + mxcp serve + ``` + +## Notes + +- Make sure to keep your API token secure and never commit it to version control. +- The plugin requires proper authentication and API permissions to work with your Confluence instance. +- All functions return JSON strings containing the requested data. \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/config.yml b/skills/mxcp-expert/assets/project-templates/confluence/config.yml new file mode 100644 index 0000000..1d8bc20 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/config.yml @@ -0,0 +1,12 @@ +mxcp: 1 + +projects: + confluence-demo: + profiles: + dev: + plugin: + config: + confluence: + url: "https://your-domain.atlassian.net/wiki" + username: "your-email@example.com" + password: "your-api-token" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/confluence/mxcp-site.yml new file mode 100644 index 0000000..d9cc0b4 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/mxcp-site.yml @@ -0,0 +1,7 @@ +mxcp: 1 +project: confluence-demo +profile: dev +plugin: + - name: confluence + module: mxcp_plugin_confluence + config: confluence diff --git a/skills/mxcp-expert/assets/project-templates/confluence/plugins/mxcp_plugin_confluence/__init__.py b/skills/mxcp-expert/assets/project-templates/confluence/plugins/mxcp_plugin_confluence/__init__.py new file mode 100644 index 0000000..bbef312 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/plugins/mxcp_plugin_confluence/__init__.py @@ -0,0 +1,172 @@ +""" +Confluence Plugin Implementation + +This module provides UDFs for interacting with Atlassian Confluence. +""" + +import json +import logging +from typing import Any, Dict, List, Optional + +from atlassian import Confluence + +from mxcp.plugins import MXCPBasePlugin, udf + +logger = logging.getLogger(__name__) + + +class MXCPPlugin(MXCPBasePlugin): + """Confluence plugin that provides content query functionality.""" + + def __init__(self, config: Dict[str, Any]): + """Initialize the Confluence plugin. + + Args: + config: Plugin configuration containing Confluence API credentials + Required keys: + - url: The base URL of your Confluence instance + - username: Your Atlassian username/email + - password: Your Atlassian API token + """ + super().__init__(config) + self.url = config.get("url", "") + self.username = config.get("username", "") + self.password = config.get("password", "") + + if not all([self.url, self.username, self.password]): + raise ValueError( + "Confluence plugin requires url, username, and password in configuration" + ) + + # Initialize Confluence client + self.confluence = Confluence( + url=self.url, username=self.username, password=self.password, cloud=True + ) + + @udf + def cql_query( + self, query: str, space_key: Optional[str] = None, max_results: Optional[int] = 50 + ) -> str: + """Execute a CQL query against Confluence. + + Args: + query: The CQL query string + space_key: Optional space key to limit the search + max_results: Maximum number of results to return (default: 50) + + Returns: + JSON string containing matching pages + """ + logger.info( + "Executing CQL query: %s in space=%s with max_results=%s", query, space_key, max_results + ) + + # Build the CQL query + cql = query + if space_key: + cql = f'space = "{space_key}" AND {cql}' + + # Execute the CQL query + results = self.confluence.cql(cql=cql, limit=max_results, expand="version,metadata.labels") + + # Transform the response to match our schema + transformed_results = [ + { + "id": page["content"]["id"], + "title": page["content"]["title"], + "space_key": page["content"]["space"]["key"], + "url": f"{self.url}/wiki/spaces/{page['content']['space']['key']}/pages/{page['content']['id']}", + "version": { + "number": page["content"]["version"]["number"], + "when": page["content"]["version"]["when"], + }, + "last_modified": page["content"]["version"]["when"], + "author": page["content"]["version"]["by"]["email"], + "labels": [ + label["name"] for label in page["content"]["metadata"]["labels"]["results"] + ], + } + for page in results["results"] + ] + + return json.dumps(transformed_results) + + @udf + def search_pages(self, query: str, limit: Optional[int] = 10) -> str: + """Search pages by keyword. + + Args: + query: Search string, e.g., 'onboarding guide' + limit: Maximum number of results to return (default: 10) + + Returns: + JSON string containing matching pages + """ + logger.info("Searching pages with query: %s, limit: %s", query, limit) + + results = self.confluence.cql(cql=f'text ~ "{query}"', limit=limit, expand="version,space") + + return json.dumps(results) + + @udf + def get_page(self, page_id: str) -> str: + """Fetch page content (storage format or rendered HTML). + + Args: + page_id: Confluence page ID + + Returns: + JSON string containing page content + """ + logger.info("Getting page content for ID: %s", page_id) + + page = self.confluence.get_page_by_id(page_id=page_id, expand="body.storage,body.view") + + return json.dumps(page) + + @udf + def get_children(self, page_id: str) -> str: + """List direct children of a page. + + Args: + page_id: Confluence page ID + + Returns: + JSON string containing child pages + """ + logger.info("Getting children for page ID: %s", page_id) + + children = self.confluence.get_child_pages(page_id=page_id, expand="version,space") + + return json.dumps(children) + + @udf + def list_spaces(self) -> str: + """Return all accessible spaces (by key and name). + + Returns: + JSON string containing list of spaces + """ + logger.info("Listing all spaces") + + spaces = self.confluence.get_all_spaces(expand="description,metadata.labels") + + return json.dumps(spaces) + + @udf + def describe_page(self, page_id: str) -> str: + """Show metadata about a page (title, author, updated, labels, etc). + + Args: + page_id: Confluence page ID + + Returns: + JSON string containing page metadata + """ + logger.info("Getting metadata for page ID: %s", page_id) + + page = self.confluence.get_page_by_id( + page_id=page_id, expand="version,space,metadata.labels" + ) + + return json.dumps(page) diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/cql_query.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/cql_query.sql new file mode 100644 index 0000000..f000542 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/cql_query.sql @@ -0,0 +1,2 @@ +-- Example CQL query endpoint +SELECT cql_query_confluence($cql, $space_key, $limit) as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/describe_page.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/describe_page.sql new file mode 100644 index 0000000..12b6791 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/describe_page.sql @@ -0,0 +1,2 @@ +-- Show metadata about a Confluence page +SELECT describe_page_confluence($page_id) as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/get_children.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/get_children.sql new file mode 100644 index 0000000..d35de3a --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/get_children.sql @@ -0,0 +1,2 @@ +-- List direct children of a Confluence page +SELECT get_children_confluence($page_id) as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/get_page.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/get_page.sql new file mode 100644 index 0000000..5514a63 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/get_page.sql @@ -0,0 +1,2 @@ +-- Get Confluence page content +SELECT get_page_confluence($page_id) as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/list_spaces.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/list_spaces.sql new file mode 100644 index 0000000..41d778d --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/list_spaces.sql @@ -0,0 +1,2 @@ +-- List all accessible Confluence spaces +SELECT list_spaces_confluence() as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/sql/search_pages.sql b/skills/mxcp-expert/assets/project-templates/confluence/sql/search_pages.sql new file mode 100644 index 0000000..87f6ff3 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/sql/search_pages.sql @@ -0,0 +1,2 @@ +-- Search Confluence pages by keyword +SELECT search_pages_confluence($query, $limit) as result; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/cql_query.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/cql_query.yml new file mode 100644 index 0000000..a092108 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/cql_query.yml @@ -0,0 +1,66 @@ +mxcp: 1 +tool: + name: cql_query + description: "Execute a CQL query against Confluence" + parameters: + - name: cql + type: string + description: | + The CQL query string to execute. + Example: 'text ~ "important documentation"' + examples: [ + 'text ~ "important documentation"', + 'type = page AND space = "TEAM"', + 'label = "documentation"' + ] + - name: space_key + type: string + description: | + The space key to search in. + Example: 'TEAM' + examples: ["TEAM", "DOCS", "PROD"] + - name: limit + type: integer + description: | + Maximum number of results to return. + Defaults to 10 if not specified. + examples: [10, 20, 50] + return: + type: array + items: + type: object + properties: + id: + type: string + description: "Page ID" + title: + type: string + description: "Page title" + space_key: + type: string + description: "Space key" + url: + type: string + description: "Page URL" + version: + type: object + properties: + number: + type: integer + description: "Version number" + when: + type: string + description: "Version timestamp" + last_modified: + type: string + description: "Last modification timestamp" + author: + type: string + description: "Page author" + labels: + type: array + items: + type: string + description: "Page labels" + source: + file: "../sql/cql_query.sql" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/describe_page.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/describe_page.yml new file mode 100644 index 0000000..daef09b --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/describe_page.yml @@ -0,0 +1,28 @@ +mxcp: 1 + +tool: + name: describe_page + description: | + Show metadata about a Confluence page. + Returns a JSON string containing page details like title, author, update date, and labels. + type: tool + annotations: + title: Describe Page + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: page_id + type: string + description: | + The ID of the page to describe. + This is typically a numeric ID found in the page URL. + examples: ["123456", "789012"] + return: + type: string + description: | + A JSON string containing the page metadata. + language: "sql" + source: + file: "../sql/describe_page.sql" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/get_children.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/get_children.yml new file mode 100644 index 0000000..b8b3df9 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/get_children.yml @@ -0,0 +1,28 @@ +mxcp: 1 + +tool: + name: get_children + description: | + List direct children of a Confluence page. + Returns a JSON string containing the child pages. + type: tool + annotations: + title: Get Children + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: page_id + type: string + description: | + The ID of the parent page. + This is typically a numeric ID found in the page URL. + examples: ["123456", "789012"] + return: + type: string + description: | + A JSON string containing an array of child pages. + language: "sql" + source: + file: "../sql/get_children.sql" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/get_page.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/get_page.yml new file mode 100644 index 0000000..619a6f5 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/get_page.yml @@ -0,0 +1,28 @@ +mxcp: 1 + +tool: + name: get_page + description: | + Fetch a Confluence page's content. + Returns a JSON string containing the page content in both storage format and rendered HTML. + type: tool + annotations: + title: Get Page + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: page_id + type: string + description: | + The ID of the page to fetch. + This is typically a numeric ID found in the page URL. + examples: ["123456", "789012"] + return: + type: string + description: | + A JSON string containing the page content. + language: "sql" + source: + file: "../sql/get_page.sql" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/list_spaces.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/list_spaces.yml new file mode 100644 index 0000000..23a7830 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/list_spaces.yml @@ -0,0 +1,21 @@ +mxcp: 1 + +tool: + name: list_spaces + description: | + List all accessible Confluence spaces. + Returns a JSON string containing space keys and names. + type: tool + annotations: + title: List Spaces + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + return: + type: string + description: | + A JSON string containing an array of spaces. + language: "sql" + source: + file: "../sql/list_spaces.sql" diff --git a/skills/mxcp-expert/assets/project-templates/confluence/tools/search_pages.yml b/skills/mxcp-expert/assets/project-templates/confluence/tools/search_pages.yml new file mode 100644 index 0000000..680b089 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/confluence/tools/search_pages.yml @@ -0,0 +1,38 @@ +mxcp: 1 + +tool: + name: search_pages + description: | + Search Confluence pages by keyword. + Returns a JSON string containing matching pages with their details. + type: tool + annotations: + title: Search Pages + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: query + type: string + description: | + The search string to find in page content. + This will search through page titles and content. + examples: [ + "onboarding guide", + "release notes", + "API documentation" + ] + - name: limit + type: integer + description: | + Maximum number of results to return. + Defaults to 10 if not specified. + examples: [10, 20, 50] + return: + type: string + description: | + A JSON string containing an array of matching pages. + language: "sql" + source: + file: "../sql/search_pages.sql" diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/.gitignore b/skills/mxcp-expert/assets/project-templates/covid_owid/.gitignore new file mode 100644 index 0000000..49f147c --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/.gitignore @@ -0,0 +1,4 @@ + +target/ +dbt_packages/ +logs/ diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/README.md b/skills/mxcp-expert/assets/project-templates/covid_owid/README.md new file mode 100644 index 0000000..0199499 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/README.md @@ -0,0 +1,149 @@ +# COVID-19 OWID Example + +This example demonstrates how to use MXCP to create a COVID-19 data analysis API. It shows how to: +- Fetch and cache COVID-19 data from Our World in Data (OWID) +- Transform data using dbt and DuckDB +- Create a natural language interface for data exploration using generic SQL tools + +## Features + +- **Comprehensive Data**: Global COVID-19 statistics from OWID +- **Data Transformation**: dbt models for efficient querying +- **Natural Language**: LLM-friendly query interface (prompt only) + +## Getting Started + +### Prerequisites + +Make sure you have the required tools installed: +```bash +# Install MXCP and dependencies +pip install mxcp dbt-core dbt-duckdb + +# Option: Install in development mode +cd /path/to/mxcp +python -m venv .venv && source .venv/bin/activate +pip install -e . +``` + +### Running the Example + +1. Navigate to the COVID example: + ```bash + cd examples/covid_owid + ``` + +2. Initialize the data: + ```bash + dbt deps + dbt run + ``` + +3. Start the MCP server: + ```bash + mxcp serve + ``` + +## 🔌 Claude Desktop Integration + +To use this example with Claude Desktop: + +### 1. Locate Claude's Configuration + +Find your Claude Desktop configuration file: +- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` +- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json` + +### 2. Configure the MCP Server + +Add this configuration to your `claude_desktop_config.json`: + +#### If you installed MXCP globally: +```json +{ + "mcpServers": { + "covid": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/absolute/path/to/mxcp/examples/covid_owid" + } + } +} +``` + +#### If you're using a virtual environment: +```json +{ + "mcpServers": { + "covid": { + "command": "/bin/bash", + "args": [ + "-c", + "cd /absolute/path/to/mxcp/examples/covid_owid && source ../../.venv/bin/activate && mxcp serve --transport stdio" + ] + } + } +} +``` + +**Important**: Replace `/absolute/path/to/mxcp` with the actual path to your MXCP installation. + +### 3. Restart Claude Desktop + +After saving the configuration, restart Claude Desktop to load the new MCP server. + +### 4. Test the Integration + +In Claude Desktop, try asking: +- "Show me COVID-19 cases in the United States for 2022" +- "Compare vaccination rates between France and Germany" +- "What were the peak hospitalization rates in the UK?" + +## 🛠️ Other MCP Clients + +This example works with any MCP-compatible tool: +- **mcp-cli**: Interactive command-line interface +- **Custom integrations**: Build your own using the MCP specification + +## Example Usage + +The LLM can help you analyze: +- Case numbers and death rates +- Vaccination progress +- Hospital occupancy +- Regional comparisons +- Policy effectiveness + +All queries are handled through the generic SQL query interface. You can: +- Use `list_tables` to see available tables +- Use `get_table_schema` to inspect table structure +- Use `execute_sql_query` to run custom SQL queries + +## Implementation Details + +The example uses: +- dbt for data transformation +- DuckDB for efficient storage and querying +- SQL analytics for complex calculations +- Type-safe parameters for filtering + +## Project Structure + +``` +covid_owid/ +├── endpoints/ # MCP endpoint definitions +│ └── prompt.yml # LLM system prompt (generic query interface only) +├── models/ # dbt transformations +│ ├── covid_data.sql # Main COVID-19 statistics +│ ├── hospitalizations.sql # Hospital/ICU data +│ └── locations.sql # Geographic data +├── mxcp-site.yml # MCP configuration +└── dbt_project.yml # dbt configuration +``` + +## Learn More + +- [OWID COVID-19 Data](https://github.com/owid/covid-19-data) - Data source +- [dbt Documentation](https://docs.getdbt.com/) - Data transformation +- [DuckDB Documentation](https://duckdb.org/docs/) - Database engine +- [MXCP Documentation](../../docs/quickstart.md) - MCP framework diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/dbt_project.yml b/skills/mxcp-expert/assets/project-templates/covid_owid/dbt_project.yml new file mode 100644 index 0000000..d1aa73c --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/dbt_project.yml @@ -0,0 +1,19 @@ +analysis-paths: +- analyses +clean-targets: +- target +- dbt_packages +config-version: 2 +macro-paths: +- macros +model-paths: +- models +name: covid_owid +profile: covid_owid_default +seed-paths: +- seeds +snapshot-paths: +- snapshots +target-path: target +test-paths: +- tests diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/models/covid_data.sql b/skills/mxcp-expert/assets/project-templates/covid_owid/models/covid_data.sql new file mode 100644 index 0000000..d734ff0 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/models/covid_data.sql @@ -0,0 +1,4 @@ +{{ config(materialized='table') }} + +select * +from read_csv_auto('https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv') \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/models/hospitalizations.sql b/skills/mxcp-expert/assets/project-templates/covid_owid/models/hospitalizations.sql new file mode 100644 index 0000000..00284ac --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/models/hospitalizations.sql @@ -0,0 +1,5 @@ +{{ config(materialized='table') }} + +select * + +from read_csv_auto('https://github.com/owid/covid-19-data/raw/master/public/data/hospitalizations/covid-hospitalizations.csv') \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/models/locations.sql b/skills/mxcp-expert/assets/project-templates/covid_owid/models/locations.sql new file mode 100644 index 0000000..1eece46 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/models/locations.sql @@ -0,0 +1,5 @@ +{{ config(materialized='table') }} + +select * + +from read_csv_auto('https://github.com/owid/covid-19-data/raw/master/public/data/hospitalizations/locations.csv') \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/models/sources.yml b/skills/mxcp-expert/assets/project-templates/covid_owid/models/sources.yml new file mode 100644 index 0000000..87bf3d6 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/models/sources.yml @@ -0,0 +1,9 @@ +version: 2 + +sources: + - name: github_covid_data + description: "COVID-19 data loaded directly from Our World in Data GitHub repository" + meta: + urls: + owid_covid_data: "https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv" + covid_hospitalizations: "https://github.com/owid/covid-19-data/raw/master/public/data/hospitalizations/covid-hospitalizations.csv" diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/covid_owid/mxcp-site.yml new file mode 100644 index 0000000..82fc282 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/mxcp-site.yml @@ -0,0 +1,5 @@ +mxcp: 1 +profile: default +project: covid_owid +sql_tools: + enabled: true \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/covid_owid/prompts/prompt.yml b/skills/mxcp-expert/assets/project-templates/covid_owid/prompts/prompt.yml new file mode 100644 index 0000000..aebb7fc --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/covid_owid/prompts/prompt.yml @@ -0,0 +1,75 @@ +mxcp: 1 + +prompt: + name: "covid_data_analyst" + description: "An AI assistant that analyzes and explains COVID-19 data from Our World in Data." + tags: ["covid", "analysis", "health", "epidemiology"] + messages: + - role: system + type: text + prompt: | + You are an expert COVID-19 data analyst with access to the Our World in Data (OWID) COVID-19 dataset. You can help users understand and analyze: + + 1. Case numbers, deaths, and testing data + 2. Vaccination rates and their impact + 3. Hospital and ICU occupancy rates + 4. Regional and country-specific trends + 5. Comparative analysis between countries + 6. Policy responses and their effectiveness + + Data Exploration Tools: + You have access to a generic query interface for exploring the COVID-19 data: + - list_tables: View all available tables in the database + - get_table_schema: Examine the structure and columns of any table + - execute_sql_query: Run custom SQL queries for data analysis + + These tools allow you to: + 1. Explore available data tables and their structure + 2. Create custom queries for specific analysis needs + 3. Perform complex aggregations and calculations + 4. Combine data from different tables + 5. Filter and sort data in any way needed + 6. Answer detailed or unusual questions from users + + Available data includes: + - Daily and cumulative case counts + - Death rates and mortality statistics + - Testing rates and positivity rates + - Vaccination data (first, second doses, boosters) + - Hospital and ICU admissions + - Demographics and population metrics + - Government response indicators + + When responding: + - Use list_tables and get_table_schema to understand available data + - Create focused SQL queries that answer the specific question + - Provide context for the numbers you present + - Explain trends and potential factors affecting the data + - Note any data limitations or gaps + - Use clear, non-technical language when possible + - Cite specific dates and sources + - Acknowledge uncertainty where it exists + - For SQL queries, explain your logic + + Example Usage: + 1. Explore available tables: + list_tables() + + 2. Understand table structure: + get_table_schema("covid_data") + + 3. Custom analysis: + execute_sql_query(" + SELECT + location, + date, + new_cases, + new_deaths, + total_vaccinations + FROM covid_data + WHERE date >= '2021-01-01' + AND location IN ('United States', 'United Kingdom') + ORDER BY date DESC + ") + + The data is sourced from Our World in Data's COVID-19 dataset, which is regularly updated and maintained by researchers at the University of Oxford. \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/earthquakes/README.md b/skills/mxcp-expert/assets/project-templates/earthquakes/README.md new file mode 100644 index 0000000..7c1ff9e --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/earthquakes/README.md @@ -0,0 +1,139 @@ +# Earthquakes Example + +This example demonstrates how to use MXCP to create a real-time earthquake data API. It shows how to: +- Query live earthquake data from the USGS API +- Transform JSON data using SQL +- Create type-safe endpoints for LLM consumption + +## Features + +- **Real-time Data**: Fetches the latest earthquake data from USGS +- **Type Safety**: Strong typing for LLM safety +- **SQL Transformations**: Complex JSON parsing and data transformation +- **Test Coverage**: Includes example tests + +## Getting Started + +### Prerequisites + +Make sure you have MXCP installed: +```bash +# Option 1: Install globally +pip install mxcp + +# Option 2: Install in development mode (if you cloned the repo) +cd /path/to/mxcp +python -m venv .venv && source .venv/bin/activate +pip install -e . +``` + +### Running the Example + +1. Navigate to the earthquakes example: + ```bash + cd examples/earthquakes + ``` + +2. Start the MCP server: + ```bash + mxcp serve + ``` + +## 🔌 Claude Desktop Integration + +To use this example with Claude Desktop: + +### 1. Locate Claude's Configuration + +Find your Claude Desktop configuration file: +- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` +- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json` + +### 2. Configure the MCP Server + +Add this configuration to your `claude_desktop_config.json`: + +#### If you installed MXCP globally: +```json +{ + "mcpServers": { + "earthquakes": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/absolute/path/to/mxcp/examples/earthquakes" + } + } +} +``` + +#### If you're using a virtual environment: +```json +{ + "mcpServers": { + "earthquakes": { + "command": "/bin/bash", + "args": [ + "-c", + "cd /absolute/path/to/mxcp/examples/earthquakes && source ../../.venv/bin/activate && mxcp serve --transport stdio" + ] + } + } +} +``` + +**Important**: Replace `/absolute/path/to/mxcp` with the actual path to your MXCP installation. + +### 3. Restart Claude Desktop + +After saving the configuration, restart Claude Desktop to load the new MCP server. + +### 4. Test the Integration + +In Claude Desktop, try asking: +- "Show me recent earthquakes above magnitude 5.0" +- "What was the strongest earthquake in the last 24 hours?" +- "List earthquakes near California" + +Claude will automatically use the earthquake data tools to answer your questions. + +## 🛠️ Other MCP Clients + +This example also works with other MCP-compatible tools: + +- **mcp-cli**: `pip install mcp-cli` then use the same server config +- **Custom integrations**: Use the MCP specification to build your own client + +## Example Usage + +Ask your LLM to: +- "Show me recent earthquakes above magnitude 5.0" +- "What was the strongest earthquake in the last 24 hours?" +- "List earthquakes near [location]" + +## Implementation Details + +The example uses: +- DuckDB's `read_json_auto` function to parse USGS GeoJSON +- SQL window functions for data analysis +- Type-safe parameters for filtering + +For more details on: +- Type system: See [Type System Documentation](../../docs/type-system.md) +- SQL capabilities: See [Integrations Documentation](../../docs/integrations.md) +- Configuration: See [Configuration Guide](../../docs/configuration.md) + +## Project Structure + +``` +earthquakes/ +├── endpoints/ +│ └── tool.yml # Endpoint definition +├── mxcp-site.yml # Project configuration +└── tests/ # Example tests +``` + +## Learn More + +- [Quickstart Guide](../../docs/quickstart.md) - Get started with MXCP +- [CLI Reference](../../docs/cli.md) - Available commands +- [Configuration](../../docs/configuration.md) - Project setup \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/earthquakes/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/earthquakes/mxcp-site.yml new file mode 100644 index 0000000..9993c7d --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/earthquakes/mxcp-site.yml @@ -0,0 +1,7 @@ +mxcp: 1 +project: earthquake-api +profile: prod +profiles: + prod: + audit: + enabled: true diff --git a/skills/mxcp-expert/assets/project-templates/earthquakes/prompts/prompt.yml b/skills/mxcp-expert/assets/project-templates/earthquakes/prompts/prompt.yml new file mode 100644 index 0000000..575d7f5 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/earthquakes/prompts/prompt.yml @@ -0,0 +1,25 @@ +mxcp: 1 + +prompt: + name: "summarize_earthquake_data" + description: "Summarizes recent earthquake activity in plain English." + tags: ["summary", "earthquake"] + parameters: + - name: top_event + type: string + description: "The most significant recent earthquake details as text" + messages: + - role: system + type: text + prompt: "You are an expert seismologist summarizing recent activity for the general public." + - role: user + type: text + prompt: | + Based on this recent event: {{ top_event }}, + please provide a brief summary of current seismic activity. + Explain the significance of the event in terms of its magnitude and location. + Include any additional details that might help the user understand the event. + Use simple language and avoid technical terms. + Keep it short and concise. + Example output: + "There was a magnitude 5.5 earthquake in San Francisco yesterday." \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/earthquakes/tools/tool.yml b/skills/mxcp-expert/assets/project-templates/earthquakes/tools/tool.yml new file mode 100644 index 0000000..bd3a61e --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/earthquakes/tools/tool.yml @@ -0,0 +1,52 @@ +mxcp: 1 + +tool: + name: "query_recent_earthquakes" + description: "Query earthquakes over a given magnitude threshold." + tags: ["earthquake", "filter"] + parameters: + - name: min_magnitude + type: number + description: "Minimum magnitude" + default: 2.5 + return: + type: array + items: + type: object + source: + code: | + WITH raw AS ( + SELECT * FROM read_json_auto('https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_day.geojson') + ), + features AS ( + SELECT + feature + FROM raw, + UNNEST(features) AS feature + ), + quakes AS ( + SELECT + feature -> 'unnest' -> 'properties' -> 'mag' AS magnitude, + feature -> 'unnest' -> 'properties' -> 'place' AS location, + feature -> 'unnest' -> 'properties' -> 'time' AS time, + feature -> 'unnest' -> 'geometry' -> 'coordinates' AS coords + FROM features + ) + SELECT + CAST(magnitude AS DOUBLE) AS magnitude, + location, + CAST(time AS BIGINT) AS time, + coords + FROM quakes + WHERE CAST(magnitude AS DOUBLE) >= $min_magnitude + ORDER BY magnitude DESC; + annotations: + title: "Query Significant Earthquakes" + readOnlyHint: true + idempotentHint: true + openWorldHint: true + tests: + - name: filter-mag + arguments: + - key: min_magnitude + value: 5.5 diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/README.md b/skills/mxcp-expert/assets/project-templates/google-calendar/README.md new file mode 100644 index 0000000..e990571 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/README.md @@ -0,0 +1,213 @@ +# Google Calendar OAuth Demo (Read-Only) + +This example demonstrates how to create safe, read-only MCP tools that interact with Google Calendar using the MXCP OAuth authentication system with the Google Calendar API. + +## Features Demonstrated + +### 1. MXCP OAuth Authentication +- Project-wide Google OAuth configuration +- Automatic token management through MXCP authentication system +- User authentication via standard OAuth 2.0 flow +- Error handling for authentication failures + +### 2. Google Calendar API Integration (Read-Only) +- `whoami` - Display information about the current authenticated Google user +- `list_calendars` - Retrieve all accessible calendars with filtering options +- `get_calendar` - Get detailed information for a specific calendar +- `list_events` - List events from a calendar with time filtering and pagination +- `get_event` - Retrieve detailed information for a specific event +- `search_events` - Search for events matching text queries +- `get_freebusy` - Check availability across multiple calendars +- Token-based API access using authenticated user context +- **Safe Design**: Only read operations - no calendar or event modifications + +## Prerequisites + +1. **Google Account**: You need a Google account with Calendar access +2. **Google Cloud Project**: Create a project in Google Cloud Console with Calendar API enabled +3. **OAuth Credentials**: Create OAuth 2.0 credentials for your application +4. **Python Dependencies**: The `google-api-python-client` and related libraries (automatically managed by MXCP) + +## Setup + +### 1. Create Google Cloud Project and Enable APIs + +1. Go to the [Google Cloud Console](https://console.cloud.google.com/) +2. Create a new project or select an existing one +3. Enable the Google Calendar API: + - Go to **APIs & Services** → **Library** + - Search for "Google Calendar API" + - Click on it and press **Enable** + +### 2. Configure OAuth Consent Screen (Required First) + +1. In Google Cloud Console, go to **APIs & Services** → **OAuth consent screen** +2. Configure the consent screen: + - **User Type**: External (for testing) or Internal (for organization use) + - **App Name**: "MXCP Google Calendar Integration" (or your preferred name) + - **User Support Email**: Your email + - **Developer Contact**: Your email +3. **Add Scopes** (under "Data access" section): + - Click "Add or Remove Scopes" + - In the scope selection dialog, search for "calendar" + - Find and select `https://www.googleapis.com/auth/calendar.readonly` (Calendar read-only access) + - Click "Update" to save the scopes +4. Save the consent screen configuration + +**Note**: The scopes are configured in the OAuth Consent Screen, not when creating the Client ID. This is why you don't see scope options when creating credentials. + +### 3. Create OAuth 2.0 Client ID + +1. Go to **APIs & Services** → **Credentials** +2. Click **Create Credentials** → **OAuth 2.0 Client IDs** +3. Configure the client: + - **Application Type**: Web application + - **Name**: "MXCP Calendar Client" (or your preferred name) + - **Authorized Redirect URIs**: Add based on your deployment: + - **Local Development**: `http://localhost:8000/google/callback` + - **Remote/Production**: `https://your-domain.com/google/callback` (replace with your actual server URL) +4. Save and note down the **Client ID** and **Client Secret** + +### 4. Configure Environment Variables + +Set your Google OAuth credentials: +```bash +export GOOGLE_CLIENT_ID="your-client-id-from-google-cloud" +export GOOGLE_CLIENT_SECRET="your-client-secret-from-google-cloud" +``` + +### 5. Configure Callback URL for Your Deployment + +The callback URL configuration depends on where your MXCP server will run: + +#### Local Development +For local development, the default configuration in `config.yml` uses `http://localhost:8000/google/callback`. This works when: +- You're running MXCP locally on your development machine +- Users authenticate from the same machine where MXCP is running + +#### Remote/Production Deployment +For remote servers or production deployments, you need to: + +1. **Update config.yml**: Modify the callback URL: + ```yaml + redirect_uris: + - "https://your-domain.com/google/callback" # Your actual URL + ``` + +2. **Update base_url**: Set the correct base URL in your config: + ```yaml + transport: + http: + base_url: https://your-domain.com # Your actual server URL + ``` + +3. **Configure OAuth Credentials**: Add the production callback URL to your Google Cloud OAuth credentials + +**Important**: +- The callback URL must be accessible from the user's browser, not just from your server +- For production deployments, Google requires HTTPS for callback URLs +- You can configure multiple callback URLs in your OAuth credentials to support both local development and production + +## Project Structure + +``` +google-calendar/ +├── mxcp-site.yml # Project metadata +├── config.yml # Server and authentication configuration +├── python/ # Python modules +│ └── google_calendar_client.py # Google Calendar API implementations +├── tools/ # Tool definitions (read-only) +│ ├── whoami.yml # Current user information +│ ├── list_calendars.yml # List accessible calendars +│ ├── get_calendar.yml # Get calendar details +│ ├── list_events.yml # List calendar events +│ ├── get_event.yml # Get event details +│ ├── search_events.yml # Search for events +│ └── get_freebusy.yml # Check availability +└── README.md # This file +``` + +## Key Concepts + +1. **MXCP OAuth Integration**: Uses MXCP's built-in Google OAuth provider for secure authentication +2. **User Context**: Access tokens are automatically managed and provided through `get_user_context()` +3. **Token-based Authentication**: Google API client is initialized with OAuth tokens instead of service account credentials +4. **Project-wide Configuration**: Authentication is configured at the project level in `config.yml` +5. **Error Handling**: Comprehensive error handling for authentication and API failures +6. **Type Safety**: Uses Python type hints and comprehensive error handling for data validation + +## Running the Example + +Once you've completed the setup above: + +1. **Start MXCP**: + ```bash + # From the examples/google-calendar directory: + MXCP_CONFIG=config.yml mxcp serve + ``` + +2. **Connect your MCP client** (e.g., Claude Desktop) to the MXCP server + +3. **Authenticate**: When the client first connects, you'll be redirected to Google to authorize the application + +4. **Use the tools**: Once authenticated, you can use all the Google Calendar tools through your MCP client + +## Example Usage + +When you use the tools through an MCP client, you can: + +### Get User Information +``` +Use the whoami tool to see your Google profile information +``` + +### Manage Calendars +``` +List all your calendars, get details for specific calendars, and check which ones you can modify +``` + +### View Calendar Events +``` +- List events: "What's on my calendar this week?" +- Search events: "Find all meetings with John" +- Get event details: "Show me details for my 3 PM meeting" +- View event information: "What meetings do I have with the marketing team?" +``` + +### Check Availability +``` +Use the freebusy tool to find available time slots across multiple calendars +``` + +## Troubleshooting + +### Authentication Errors +- **"No user context available"**: User needs to authenticate first by running `mxcp serve` and completing OAuth flow +- **"No Google access token found"**: Authentication was incomplete or token expired - re-authenticate +- **OAuth Credentials Issues**: Verify your `GOOGLE_CLIENT_ID` and `GOOGLE_CLIENT_SECRET` are correct +- **Callback URL Mismatch**: Ensure the callback URL in your Google Cloud OAuth credentials matches where your MXCP server is accessible +- **API Not Enabled**: Make sure the Google Calendar API is enabled in your Google Cloud project + +### API Errors +- **403 Forbidden**: Check that the Calendar API is enabled and your OAuth scopes include calendar access +- **404 Not Found**: Verify calendar IDs and event IDs are correct and accessible to the authenticated user +- **Rate Limiting**: Google Calendar API has rate limits - implement appropriate retry logic if needed + +### OAuth Setup Issues +- **Consent Screen**: Make sure your OAuth consent screen is properly configured with the correct scopes +- **Redirect URI**: The redirect URI must exactly match your MXCP server's accessible address +- **Scopes**: Ensure your OAuth configuration includes `https://www.googleapis.com/auth/calendar.readonly` scope + +## Next Steps + +This example demonstrates a comprehensive set of read-only Google Calendar integration tools. You could extend it with additional features like: +- Advanced calendar filtering and search capabilities +- Integration with other Google Workspace services (read-only) +- Calendar analytics and reporting +- Event pattern analysis and insights +- Multi-calendar comparison and availability analysis + +**Note**: This example is intentionally read-only for safety. If you need write operations (create, update, delete), you would need to: +- Change the OAuth scope to `https://www.googleapis.com/auth/calendar` (full access) +- Add appropriate write functions with proper validation and error handling +- Implement additional safety measures and user confirmations diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/config.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/config.yml new file mode 100644 index 0000000..df8b088 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/config.yml @@ -0,0 +1,22 @@ +mxcp: 1 +transport: + http: + port: 8000 + host: 0.0.0.0 + # Set base_url to your server's public URL for production + base_url: http://localhost:8000 + +projects: + google-calendar: + profiles: + default: + # OAuth Authentication Configuration + auth: + provider: google + google: + client_id: "${GOOGLE_CLIENT_ID}" + client_secret: "${GOOGLE_CLIENT_SECRET}" + scope: "https://www.googleapis.com/auth/calendar.readonly https://www.googleapis.com/auth/userinfo.profile https://www.googleapis.com/auth/userinfo.email" + callback_path: "/google/callback" + auth_url: "https://accounts.google.com/o/oauth2/v2/auth" + token_url: "https://oauth2.googleapis.com/token" diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/mxcp-site.yml new file mode 100644 index 0000000..4eb264a --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/mxcp-site.yml @@ -0,0 +1,3 @@ +mxcp: 1 +project: google-calendar +profile: default diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/python/google_calendar_client.py b/skills/mxcp-expert/assets/project-templates/google-calendar/python/google_calendar_client.py new file mode 100644 index 0000000..4148760 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/python/google_calendar_client.py @@ -0,0 +1,804 @@ +""" +Google Calendar MCP client implementation using mxcp OAuth authentication. + +This module provides Google Calendar API integration with: +- OAuth 2.0 authentication via mxcp framework +- Thread-safe client caching for performance +- Simplified time handling for LLM consumption +- Comprehensive error handling and user-friendly messages +- Full type safety with Pydantic models +""" + +# Required for union syntax (|) in type annotations with runtime objects like threading.Lock +# Without this, Python tries to evaluate "threading.Lock | None" at runtime, which fails +from __future__ import annotations + +import logging +import threading +from datetime import date, datetime, timezone +from functools import wraps +from typing import Any + +from google.auth.exceptions import RefreshError +from google.oauth2.credentials import Credentials +from googleapiclient.discovery import Resource, build # type: ignore[import-untyped] +from googleapiclient.errors import HttpError # type: ignore[import-untyped] + +from mxcp.runtime import on_init, on_shutdown +from mxcp.sdk.auth.context import get_user_context + +# ============================================================================= +# TIME CONVERSION UTILITIES +# ============================================================================= + + +def _datetime_to_google_time( + dt: datetime, all_day: bool = False, time_zone: str | None = None +) -> dict[str, Any]: + """ + Convert datetime object to Google Calendar API time format. + + Args: + dt: Python datetime object (should be timezone-aware) + all_day: Whether this represents an all-day event + time_zone: Optional timezone override + + Returns: + Google API time object: {"dateTime": "...", "timeZone": "..."} + or {"date": "YYYY-MM-DD"} for all-day events + """ + if all_day: + # For all-day events, use date format + return {"date": dt.date().isoformat()} + else: + # For timed events, use dateTime format + time_obj = {"dateTime": dt.isoformat()} + + # Add timezone if specified or if datetime has timezone info + if time_zone: + time_obj["timeZone"] = time_zone + elif dt.tzinfo: + # Extract timezone name from datetime object if possible + tz_name = getattr(dt.tzinfo, "zone", None) or str(dt.tzinfo) + if tz_name != "UTC" and "+" not in tz_name and "-" not in tz_name: + time_obj["timeZone"] = tz_name + else: + # If no timezone info is available, try to get user's timezone + try: + user_timezone = _get_user_timezone() + logger.warning( + f"Datetime object has no timezone info, using user timezone: {user_timezone}" + ) + time_obj["timeZone"] = user_timezone + except ValueError as e: + raise ValueError( + f"Datetime object has no timezone information and cannot determine timezone from calendar. " + f"Please either: 1) Use timezone-aware datetime objects, or 2) Specify the time_zone parameter. " + f"Original error: {e}" + ) from e + + return time_obj + + +def _get_user_timezone() -> str: + """Get the user's timezone from their primary calendar. + + This function only tries to get the timezone from the user's primary calendar. + If that fails, it raises an exception to force explicit timezone specification. + + Results are cached to avoid repeated lookups. + + Returns: + IANA timezone identifier from user's primary calendar + + Raises: + ValueError: If timezone cannot be determined from primary calendar + """ + global _user_timezone_cache, _timezone_cache_lock + + # Check cache first + if _timezone_cache_lock and _user_timezone_cache: + with _timezone_cache_lock: + if _user_timezone_cache: + return _user_timezone_cache + + try: + # Get timezone from user's primary calendar + calendar_info = get_calendar("primary") + if calendar_info and calendar_info.get("timeZone"): + user_timezone: str = calendar_info["timeZone"] + logger.debug(f"Using timezone from primary calendar: {user_timezone}") + # Cache the result + if _timezone_cache_lock: + with _timezone_cache_lock: + _user_timezone_cache = user_timezone + return user_timezone + else: + raise ValueError("Primary calendar does not have timezone information") + + except Exception as e: + logger.debug(f"Could not get timezone from primary calendar: {e}") + raise ValueError( + "Cannot determine timezone from primary calendar. " + "Please specify the time_zone parameter explicitly in your function call." + ) from e + + +def _google_time_to_datetime(google_time: dict[str, Any]) -> tuple[datetime, bool]: + """ + Convert Google Calendar API time format to datetime object. + + Args: + google_time: Google API time object + + Returns: + Tuple of (datetime_object, is_all_day) + """ + if "date" in google_time: + # All-day event - convert date to datetime at midnight UTC + date_obj = date.fromisoformat(google_time["date"]) + dt = datetime.combine(date_obj, datetime.min.time(), tzinfo=timezone.utc) + return dt, True + elif "dateTime" in google_time: + # Timed event - parse ISO datetime + dt = datetime.fromisoformat(google_time["dateTime"]) + return dt, False + else: + raise ValueError(f"Invalid Google time format: {google_time}") + + +def _convert_event_to_simplified(google_event: dict[str, Any]) -> dict[str, Any]: + """ + Convert Google Calendar API event to our simplified EventInfo format. + + Args: + google_event: Raw event from Google Calendar API + + Returns: + Event in simplified format matching EventInfo model + """ + # Convert start/end times + start_dt, start_all_day = _google_time_to_datetime(google_event["start"]) + end_dt, end_all_day = _google_time_to_datetime(google_event["end"]) + + # Ensure both times have same all-day status + all_day = start_all_day and end_all_day + + simplified_event = { + "id": google_event["id"], + "summary": google_event.get("summary", ""), + "description": google_event.get("description"), + "location": google_event.get("location"), + "start_time": start_dt, + "end_time": end_dt, + "all_day": all_day, + "time_zone": google_event.get("start", {}).get("timeZone"), + "status": google_event.get("status", "confirmed"), + "htmlLink": google_event.get("htmlLink", ""), + "created": datetime.fromisoformat(google_event["created"].replace("Z", "+00:00")), + "updated": datetime.fromisoformat(google_event["updated"].replace("Z", "+00:00")), + "calendar_id": google_event.get("calendarId", "unknown"), # Added by our code + "etag": google_event.get("etag"), + } + + # Convert attendees if present + if "attendees" in google_event: + simplified_event["attendees"] = [ + { + "email": att["email"], + "displayName": att.get("displayName"), + "responseStatus": att.get("responseStatus", "needsAction"), + "optional": att.get("optional", False), + "resource": att.get("resource", False), + "comment": att.get("comment"), + "additionalGuests": att.get("additionalGuests", 0), + } + for att in google_event["attendees"] + ] + + # Convert creator/organizer if present + for role in ["creator", "organizer"]: + if role in google_event: + simplified_event[role] = { + "email": google_event[role].get("email"), + "displayName": google_event[role].get("displayName"), + "self": google_event[role].get("self", False), + } + + # Handle recurrence rules + if "recurrence" in google_event: + simplified_event["recurrence"] = google_event["recurrence"] + + # Handle reminders + if "reminders" in google_event: + reminders = google_event["reminders"] + simplified_event["reminders"] = { + "useDefault": reminders.get("useDefault", True), + "overrides": reminders.get("overrides"), + } + + # Handle transparency and visibility + simplified_event["transparency"] = google_event.get("transparency", "opaque") + simplified_event["visibility"] = google_event.get("visibility", "default") + + return simplified_event + + +def _convert_simplified_to_google_event(simplified_event: dict[str, Any]) -> dict[str, Any]: + """ + Convert simplified event format to Google Calendar API format. + + Args: + simplified_event: Event in our simplified format + + Returns: + Event in Google Calendar API format + """ + google_event = { + "summary": simplified_event["summary"], + "start": _datetime_to_google_time( + simplified_event["start_time"], + simplified_event.get("all_day", False), + simplified_event.get("time_zone"), + ), + "end": _datetime_to_google_time( + simplified_event["end_time"], + simplified_event.get("all_day", False), + simplified_event.get("time_zone"), + ), + } + + # Add optional fields + optional_fields = ["description", "location"] + for field in optional_fields: + if field in simplified_event and simplified_event[field] is not None: + google_event[field] = simplified_event[field] + + # Convert attendees + if "attendees" in simplified_event and simplified_event["attendees"]: + google_event["attendees"] = [ + ( + {"email": str(email)} + if isinstance(email, str) + else { + "email": ( + str(email) if isinstance(email, str) else str(attendee.get("email", "")) + ), + "displayName": attendee.get("displayName"), + "optional": attendee.get("optional", False), + "resource": attendee.get("resource", False), + "comment": attendee.get("comment"), + "additionalGuests": attendee.get("additionalGuests", 0), + } + ) + for email in simplified_event["attendees"] + for attendee in [email if isinstance(email, dict) else {"email": email}] + ] + + # Add transparency and visibility if specified + if "transparency" in simplified_event: + google_event["transparency"] = simplified_event["transparency"] + if "visibility" in simplified_event: + google_event["visibility"] = simplified_event["visibility"] + if "recurrence" in simplified_event: + google_event["recurrence"] = simplified_event["recurrence"] + + # Add reminders if specified + if "reminders" in simplified_event and simplified_event["reminders"]: + google_event["reminders"] = simplified_event["reminders"] + + return google_event + + +# ============================================================================= +# THREAD-SAFE CLIENT CACHING +# ============================================================================= + +# Thread-safe cache for Google Calendar service clients +_client_cache: dict[str, Resource] | None = None +_cache_lock: threading.Lock | None = None + +# Global timezone cache to avoid repeated lookups +_user_timezone_cache: str | None = None +_timezone_cache_lock: threading.Lock | None = None + + +@on_init +def init_client_cache() -> None: + """Initialize the Google Calendar client cache and timezone cache.""" + global _client_cache, _cache_lock, _timezone_cache_lock + _client_cache = {} + _cache_lock = threading.Lock() + _timezone_cache_lock = threading.Lock() + + +@on_shutdown +def clear_client_cache() -> None: + """Clear the Google Calendar client cache and timezone cache.""" + global _client_cache, _cache_lock, _user_timezone_cache, _timezone_cache_lock + _client_cache = None + _cache_lock = None + _user_timezone_cache = None + _timezone_cache_lock = None + + +def _get_cache_key(context: Any) -> str | None: + """Generate cache key based on user context.""" + if not context: + return None + + # Use user ID as cache key for per-user client isolation + user_id = getattr(context, "user_id", None) or getattr(context, "id", None) + if user_id: + return f"gcal:{user_id}" + + return None + + +def _get_google_credentials() -> Credentials: + """Get Google OAuth credentials from mxcp user context.""" + context = get_user_context() + if not context or not context.external_token: + raise ValueError("No user context available. User must be authenticated.") + + # Create Google credentials object with OAuth token + credentials = Credentials(token=context.external_token) # type: ignore[no-untyped-call] + return credentials + + +# ============================================================================= +# LOGGING & ERROR HANDLING +# ============================================================================= + +# Set up comprehensive logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def with_session_retry(func: Any) -> Any: + """ + Decorator for handling OAuth token refresh and API errors with user-friendly messages. + + Wraps functions to automatically handle: + - OAuth token refresh failures (RefreshError) + - Google API HTTP errors with specific status codes + - Client cache invalidation on auth failures + - Comprehensive error logging for debugging + """ + + @wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + try: + logger.info(f"Executing {func.__name__} with args={args}, kwargs={kwargs}") + result = func(*args, **kwargs) + logger.info(f"Successfully completed {func.__name__}") + return result + + except RefreshError as e: + # OAuth token has expired and cannot be refreshed + logger.warning(f"OAuth token refresh failed in {func.__name__}: {e}") + clear_client_cache() # Clear cache to force re-authentication + error_msg = ( + "Your Google Calendar access has expired. Please re-authenticate to continue." + ) + logger.error(f"Authentication error: {error_msg}") + raise ValueError(error_msg) from e + + except HttpError as e: + # Handle specific Google API errors with detailed logging + status = e.resp.status + error_details = str(e) + logger.error( + f"Google API HttpError in {func.__name__}: status={status}, details={error_details}" + ) + + # Clear cache on authentication errors + if status in [401, 403]: + context = get_user_context() + cache_key = _get_cache_key(context) + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + _client_cache.pop(cache_key, None) + + # Just forward the original Google API error - it's already clear and actionable + raise ValueError(f"Google Calendar API error: {error_details}") from e + + # ValidationError removed since we no longer use Pydantic models + + except ValueError as e: + # Re-raise ValueError (these are user-friendly messages) + logger.warning(f"User error in {func.__name__}: {e}") + raise + + except Exception as e: + # Catch-all for unexpected errors + logger.error( + f"Unexpected error in {func.__name__}: {type(e).__name__}: {e}", exc_info=True + ) + raise ValueError(f"An unexpected error occurred: {str(e)}") from e + + return wrapper + + +def _get_google_calendar_client() -> Resource: + """ + Get cached Google Calendar API client or create new one with OAuth authentication. + + Uses per-user caching for performance and proper multi-user isolation. + """ + try: + # Get authenticated user context + context = get_user_context() + if not context: + raise ValueError("No user context available. User must be authenticated.") + + # Check cache first + cache_key = _get_cache_key(context) + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + if cache_key in _client_cache: + logging.info("Using cached Google Calendar client") + return _client_cache[cache_key] + + logging.info("Creating new Google Calendar client") + # Create new authenticated client + credentials = _get_google_credentials() + service = build( + serviceName="calendar", + version="v3", + credentials=credentials, + cache_discovery=True, # Cache API discovery documents + num_retries=3, # Retry transient failures + ) + + # Cache the client for this user + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + _client_cache[cache_key] = service + + return service + + except Exception as e: + raise ValueError(f"Failed to initialize Google Calendar client: {str(e)}") from e + + +# ============================================================================= +# MCP TOOL FUNCTIONS +# ============================================================================= + + +@with_session_retry +def whoami() -> dict[str, Any]: + """ + Get information about the currently authenticated user. + + Returns: + UserInfo object with user profile data from Google OAuth + + Note: + Uses OAuth profile information, no additional API calls required + """ + context = get_user_context() + if not context: + raise ValueError("No user context available. User must be authenticated.") + + # Extract user information from OAuth profile + raw_profile = context.raw_profile or {} + + # Return plain dictionary following standard MXCP pattern + return { + "id": raw_profile.get("sub") or raw_profile.get("id", "unknown"), + "email": raw_profile.get("email", "unknown@example.com"), + "name": raw_profile.get("name", "Unknown User"), + "given_name": raw_profile.get("given_name"), + "family_name": raw_profile.get("family_name"), + "picture": raw_profile.get("picture"), + "locale": raw_profile.get("locale"), + "verified_email": raw_profile.get("email_verified"), + } + + +@with_session_retry +def list_calendars( + show_hidden: bool = False, + show_deleted: bool = False, + max_results: int = 100, + min_access_role: str | None = None, +) -> list[dict[str, Any]]: + """ + List all calendars accessible to the authenticated user. + + Args: + show_hidden: Include hidden calendars in results + show_deleted: Include deleted calendars in results + max_results: Maximum number of calendars to return (1-250) + min_access_role: Filter by minimum access level + + Returns: + List of CalendarInfo objects with user's accessible calendars + + Raises: + ValueError: If user is not authenticated or parameters are invalid + """ + service = _get_google_calendar_client() + + # Build parameters + params: dict[str, Any] = { + "maxResults": min(max_results, 250), + "showHidden": show_hidden, + "showDeleted": show_deleted, + } + + if min_access_role: + params["minAccessRole"] = min_access_role + + # Execute API call + result = service.calendarList().list(**params).execute() + + # Return plain dictionaries following standard MXCP pattern + calendars = [] + for cal in result.get("items", []): + calendar_dict = { + "id": cal["id"], + "summary": cal.get("summary", ""), + "description": cal.get("description"), + "timeZone": cal.get("timeZone", "UTC"), + "accessRole": cal.get("accessRole", "reader"), + "primary": cal.get("primary", False), + "backgroundColor": cal.get("backgroundColor"), + "foregroundColor": cal.get("foregroundColor"), + "selected": cal.get("selected", False), + "hidden": cal.get("hidden", False), + "defaultReminders": cal.get("defaultReminders"), + } + calendars.append(calendar_dict) + + return calendars + + +@with_session_retry +def get_calendar(calendar_id: str) -> dict[str, Any]: + """ + Get detailed information for a specific calendar. + + Args: + calendar_id: Calendar identifier or 'primary' for main calendar + + Returns: + CalendarInfo object with calendar details + + Raises: + ValueError: If calendar_id is invalid or user lacks access + """ + service = _get_google_calendar_client() + + try: + result = service.calendarList().get(calendarId=calendar_id).execute() + except HttpError as e: + if e.resp.status == 404: + raise ValueError( + f"Calendar '{calendar_id}' not found or you don't have access to it" + ) from e + raise + + # Return plain dictionary following standard MXCP pattern + return { + "id": result["id"], + "summary": result.get("summary", ""), + "description": result.get("description"), + "timeZone": result.get("timeZone", "UTC"), + "accessRole": result.get("accessRole", "reader"), + "primary": result.get("primary", False), + "backgroundColor": result.get("backgroundColor"), + "foregroundColor": result.get("foregroundColor"), + "selected": result.get("selected", False), + "hidden": result.get("hidden", False), + "defaultReminders": result.get("defaultReminders"), + } + + +@with_session_retry +def list_events( + calendar_id: str = "primary", + time_min: datetime | None = None, + time_max: datetime | None = None, + max_results: int = 250, + single_events: bool = True, + order_by: str = "startTime", + page_token: str | None = None, +) -> dict[str, Any]: + """ + List events from a specific calendar with optional time filtering. + + Args: + calendar_id: Calendar to query ('primary' or specific calendar ID) + time_min: Lower bound for event start times (inclusive) + time_max: Upper bound for event start times (exclusive) + max_results: Maximum number of events to return (1-2500) + single_events: Whether to expand recurring events into instances + order_by: Sort order - 'startTime' or 'updated' + page_token: Token for pagination + + Returns: + EventSearchResult with events and pagination info + """ + service = _get_google_calendar_client() + + # Build parameters + params = { + "calendarId": calendar_id, + "maxResults": min(max_results, 2500), + "singleEvents": single_events, + "orderBy": order_by, + } + + if time_min: + params["timeMin"] = time_min.isoformat() + if time_max: + params["timeMax"] = time_max.isoformat() + if page_token: + params["pageToken"] = page_token + + # Execute API call + result = service.events().list(**params).execute() + + # Convert events to simplified format + events = [] + for event in result.get("items", []): + event["calendarId"] = calendar_id # Add calendar_id to event + simplified_event = _convert_event_to_simplified(event) + events.append(simplified_event) + + # Return plain dictionary following standard MXCP pattern + return { + "events": events, + "next_page_token": result.get("nextPageToken"), + "total_results": len(events), + } + + +@with_session_retry +def get_event(calendar_id: str, event_id: str) -> dict[str, Any]: + """ + Retrieve detailed information for a specific event. + + Args: + calendar_id: Calendar containing the event + event_id: Event identifier + + Returns: + EventInfo object with complete event details + + Raises: + ValueError: If event not found or user lacks access + """ + service = _get_google_calendar_client() + + try: + result = service.events().get(calendarId=calendar_id, eventId=event_id).execute() + except HttpError as e: + if e.resp.status == 404: + raise ValueError(f"Event '{event_id}' not found in calendar '{calendar_id}'") from e + raise + + # Add calendar_id to event + result["calendarId"] = calendar_id + simplified_event = _convert_event_to_simplified(result) + + return simplified_event + + +@with_session_retry +def search_events( + q: str, + calendar_id: str = "primary", + time_min: datetime | None = None, + time_max: datetime | None = None, + max_results: int = 250, + page_token: str | None = None, +) -> dict[str, Any]: + """ + Search for events matching a text query. + + Args: + q: Free text search query (searches title, description, location, attendees) + calendar_id: Calendar to search ('primary' or specific calendar ID) + time_min: Earliest event start time to include + time_max: Latest event start time to include + max_results: Maximum number of events to return + page_token: Token for pagination + + Returns: + EventSearchResult with matching events and pagination info + """ + service = _get_google_calendar_client() + + # Build parameters + params = { + "calendarId": calendar_id, + "q": q, + "maxResults": min(max_results, 2500), + "singleEvents": True, + "orderBy": "startTime", + } + + if time_min: + params["timeMin"] = time_min.isoformat() + if time_max: + params["timeMax"] = time_max.isoformat() + if page_token: + params["pageToken"] = page_token + + # Execute API call + result = service.events().list(**params).execute() + + # Convert events to simplified format + events = [] + for event in result.get("items", []): + event["calendarId"] = calendar_id # Add calendar_id to event + simplified_event = _convert_event_to_simplified(event) + events.append(simplified_event) + + # Return plain dictionary following standard MXCP pattern + return { + "events": events, + "next_page_token": result.get("nextPageToken"), + "total_results": len(events), + } + + +@with_session_retry +def get_freebusy(calendar_ids: list[str], time_min: datetime, time_max: datetime) -> dict[str, Any]: + """ + Check free/busy status across multiple calendars. + + Args: + calendar_ids: List of calendar IDs to check (use 'primary' for main calendar) + time_min: Start time for availability check + time_max: End time for availability check + + Returns: + FreeBusyResponse with busy periods for each calendar + + Note: + Useful for finding meeting slots and checking availability before scheduling + """ + if time_min >= time_max: + raise ValueError("time_max must be after time_min") + + service = _get_google_calendar_client() + + # Build request + request_body = { + "timeMin": time_min.isoformat(), + "timeMax": time_max.isoformat(), + "items": [{"id": cal_id} for cal_id in calendar_ids], + } + + # Execute API call + result = service.freebusy().query(body=request_body).execute() + + # Convert to plain dictionary format following standard MXCP pattern + calendars = [] + for calendar_id in calendar_ids: + calendar_data = result.get("calendars", {}).get(calendar_id, {}) + + # Convert busy times to plain dictionaries + busy_times = [] + for busy_period in calendar_data.get("busy", []): + busy_times.append( + { + "start": busy_period["start"], + "end": busy_period["end"], + } + ) + + calendars.append( + {"calendar_id": calendar_id, "busy": busy_times, "errors": calendar_data.get("errors")} + ) + + return { + "time_min": time_min.isoformat(), + "time_max": time_max.isoformat(), + "calendars": calendars, + } diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_calendar.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_calendar.yml new file mode 100644 index 0000000..9dffb12 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_calendar.yml @@ -0,0 +1,63 @@ +mxcp: 1 +tool: + name: get_calendar + title: Get Calendar Details + description: | + Get detailed information for a specific calendar by ID. + Returns calendar metadata including timezone, access role, and display properties. + + Example usage: + - "Get details for my primary calendar" + - "Show me information about the work@company.com calendar" + - "What timezone is my calendar set to?" + tags: + - google-calendar + - calendars + - get + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: calendar_id + type: string + description: "Calendar identifier or 'primary' for main calendar" + default: "primary" + examples: ["primary", "work@company.com", "team@company.com"] + return: + type: object + description: "Calendar details and metadata" + properties: + id: + type: string + description: "Calendar identifier" + summary: + type: string + description: "Calendar name/title" + description: + type: string + description: "Calendar description" + timeZone: + type: string + description: "IANA timezone identifier" + accessRole: + type: string + description: "User's access level" + primary: + type: boolean + description: "Whether this is user's primary calendar" + backgroundColor: + type: string + description: "Background color hex code" + foregroundColor: + type: string + description: "Foreground color hex code" + selected: + type: boolean + description: "Whether calendar is selected in UI" + hidden: + type: boolean + description: "Whether calendar is hidden from list" + required: ["id", "summary", "timeZone", "accessRole"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_event.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_event.yml new file mode 100644 index 0000000..e7610dd --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_event.yml @@ -0,0 +1,96 @@ +mxcp: 1 +tool: + name: get_event + title: Get Event Details + description: | + Retrieve detailed information for a specific event by ID. + Returns complete event data in simplified format optimized for LLM use. + + Example usage: + - "Show me details for event abc123 in my primary calendar" + - "Get full information about that meeting I mentioned" + - "What are the attendees for event xyz789?" + tags: + - google-calendar + - events + - get + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: calendar_id + type: string + description: "Calendar containing the event" + examples: ["primary", "work@company.com", "team-calendar@company.com"] + - name: event_id + type: string + description: "Event identifier" + examples: ["abc123def456", "event_id_example", "recurring_event_20240115T090000Z"] + return: + type: object + description: "Complete event details" + properties: + id: + type: string + description: "Event identifier" + summary: + type: string + description: "Event title" + description: + type: string + description: "Event description" + location: + type: string + description: "Event location" + start_time: + type: string + format: date-time + description: "Event start time" + end_time: + type: string + format: date-time + description: "Event end time" + all_day: + type: boolean + description: "Whether this is an all-day event" + time_zone: + type: string + description: "Event timezone" + attendees: + type: array + description: "Event attendees" + items: + type: object + properties: + email: + type: string + description: "Attendee email" + displayName: + type: string + description: "Attendee name" + responseStatus: + type: string + description: "Response status" + status: + type: string + description: "Event status" + htmlLink: + type: string + description: "Google Calendar web URL" + created: + type: string + format: date-time + description: "Creation timestamp" + updated: + type: string + format: date-time + description: "Last update timestamp" + recurrence: + type: array + description: "Recurrence rules" + items: + type: string + required: ["id", "summary", "start_time", "end_time", "htmlLink", "status"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_freebusy.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_freebusy.yml new file mode 100644 index 0000000..6c6bef1 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/get_freebusy.yml @@ -0,0 +1,83 @@ +mxcp: 1 +tool: + name: get_freebusy + title: Check Calendar Availability + description: | + Check free/busy status across multiple calendars for a specified time range. + Useful for finding meeting slots and checking availability before scheduling. + + Example usage: + - "Check my availability tomorrow from 9 AM to 5 PM" + - "Find free time slots across my work and personal calendars" + - "When am I free for a meeting this week?" + - "Check availability for multiple team members' calendars" + tags: + - google-calendar + - freebusy + - availability + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: calendar_ids + type: array + description: "List of calendar IDs to check (use 'primary' for main calendar)" + items: + type: string + description: "Calendar identifier" + minItems: 1 + examples: [["primary"], ["primary", "work@company.com"], ["team@company.com", "resources@company.com"]] + - name: time_min + type: string + format: date-time + description: "Start time for availability check (RFC3339 format)" + examples: ["2024-01-15T09:00:00Z", "2024-01-15T09:00:00-08:00"] + - name: time_max + type: string + format: date-time + description: "End time for availability check (RFC3339 format)" + examples: ["2024-01-15T17:00:00Z", "2024-01-15T17:00:00-08:00"] + return: + type: object + description: "Free/busy information for requested calendars" + properties: + time_min: + type: string + format: date-time + description: "Query start time" + time_max: + type: string + format: date-time + description: "Query end time" + calendars: + type: array + description: "Per-calendar availability information" + items: + type: object + properties: + calendar_id: + type: string + description: "Calendar identifier" + busy: + type: array + description: "Busy time periods" + items: + type: object + properties: + start: + type: string + format: date-time + description: "Busy period start" + end: + type: string + format: date-time + description: "Busy period end" + errors: + type: array + description: "API errors for this calendar" + items: + type: object + required: ["time_min", "time_max", "calendars"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_calendars.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_calendars.yml new file mode 100644 index 0000000..bd60d77 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_calendars.yml @@ -0,0 +1,84 @@ +mxcp: 1 +tool: + name: list_calendars + title: List Google Calendars + description: | + List all calendars accessible to the authenticated user. + Returns calendars with their access permissions, timezones, and display properties. + + Example usage: + - "Show me all my calendars" + - "List calendars I can write to" (with min_access_role: "writer") + - "What calendars do I have access to?" + - "Show me my work calendars" (then filter by name) + tags: + - google-calendar + - calendars + - list + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: show_hidden + type: boolean + description: "Include hidden calendars in results" + default: false + examples: [false, true] + - name: show_deleted + type: boolean + description: "Include deleted calendars in results" + default: false + examples: [false, true] + - name: max_results + type: integer + description: "Maximum number of calendars to return" + default: 100 + minimum: 1 + maximum: 250 + examples: [10, 50, 100] + - name: min_access_role + type: string + description: "Filter by minimum access role" + enum: ["freeBusyReader", "owner", "reader", "writer"] + default: null + examples: ["reader", "writer", "owner"] + return: + type: array + description: "List of accessible calendars" + items: + type: object + properties: + id: + type: string + description: "Calendar identifier (email address or calendar ID)" + summary: + type: string + description: "Calendar name/title" + description: + type: string + description: "Calendar description" + timeZone: + type: string + description: "IANA timezone identifier" + accessRole: + type: string + description: "User's access level" + primary: + type: boolean + description: "Whether this is user's primary calendar" + backgroundColor: + type: string + description: "Background color hex code" + foregroundColor: + type: string + description: "Foreground color hex code" + selected: + type: boolean + description: "Whether calendar is selected in UI" + hidden: + type: boolean + description: "Whether calendar is hidden from list" + required: ["id", "summary", "timeZone", "accessRole"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_events.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_events.yml new file mode 100644 index 0000000..a4eb5bd --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/list_events.yml @@ -0,0 +1,153 @@ +mxcp: 1 +tool: + name: list_events + title: List Calendar Events + description: | + List events from a specific calendar with optional time filtering and pagination. + Returns events in simplified format optimized for LLM consumption. + + Example usage: + - "What's on my calendar today?" (with time_min/time_max for today) + - "Show me this week's meetings" (with time range for current week) + - "List all events in my work calendar" (with specific calendar_id) + - "What meetings do I have next month?" (with future time range) + tags: + - google-calendar + - events + - list + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: calendar_id + type: string + description: "Calendar to query ('primary' or specific calendar ID)" + default: "primary" + examples: ["primary", "work@company.com", "team-calendar@company.com"] + - name: time_min + type: string + format: date-time + description: "Lower bound for event start times (RFC3339 format)" + default: null + examples: ["2024-01-15T00:00:00Z", "2024-01-15T09:00:00-08:00"] + - name: time_max + type: string + format: date-time + description: "Upper bound for event start times (RFC3339 format)" + default: null + examples: ["2024-01-15T23:59:59Z", "2024-01-15T17:00:00-08:00"] + - name: max_results + type: integer + description: "Maximum number of events to return" + default: 250 + minimum: 1 + maximum: 2500 + examples: [10, 50, 250] + - name: single_events + type: boolean + description: "Whether to expand recurring events into instances" + default: true + examples: [true, false] + - name: order_by + type: string + description: "Sort order for events" + enum: ["startTime", "updated"] + default: "startTime" + examples: ["startTime", "updated"] + - name: page_token + type: string + description: "Token for pagination" + default: null + examples: ["CAESGjBpNDd2Nmp2Zml2cXRwYjBpOXA", "next_page_token_example"] + return: + type: object + description: "Event search results with pagination" + properties: + events: + type: array + description: "Matching events" + items: + type: object + description: "Complete event information with simplified time handling" + properties: + id: + type: string + description: "Event identifier" + summary: + type: string + description: "Event title" + description: + type: string + description: "Event description" + location: + type: string + description: "Event location" + start_time: + type: string + format: date-time + description: "Event start time (timezone-aware)" + end_time: + type: string + format: date-time + description: "Event end time (timezone-aware)" + all_day: + type: boolean + description: "Whether this is an all-day event" + time_zone: + type: string + description: "Event timezone (if different from calendar)" + attendees: + type: array + description: "Event attendees" + items: + type: object + creator: + type: object + description: "Event creator" + organizer: + type: object + description: "Event organizer" + status: + type: string + description: "Event status" + htmlLink: + type: string + description: "Google Calendar web URL for this event" + created: + type: string + format: date-time + description: "Event creation timestamp (timezone-aware)" + updated: + type: string + format: date-time + description: "Last update timestamp (timezone-aware)" + recurrence: + type: array + description: "Recurrence rules in RRULE format" + items: + type: string + reminders: + type: object + description: "Reminder settings" + transparency: + type: string + description: "Event transparency" + visibility: + type: string + description: "Event visibility" + calendar_id: + type: string + description: "Calendar containing this event" + etag: + type: string + description: "Event ETag for change detection" + next_page_token: + type: string + description: "Token for pagination" + total_results: + type: integer + description: "Total number of matching events" + required: ["events"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/search_events.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/search_events.yml new file mode 100644 index 0000000..623a33c --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/search_events.yml @@ -0,0 +1,97 @@ +mxcp: 1 +tool: + name: search_events + title: Search Calendar Events + description: | + Search for events matching a text query. Searches across event titles, descriptions, + locations, and attendee information with optional time filtering. + + Example usage: + - "Find all meetings with John" (q: "John") + - "Search for events about project Alpha" (q: "project Alpha") + - "Find meetings in the conference room" (q: "conference room") + - "Show me all standup meetings this month" (q: "standup" with time range) + tags: + - google-calendar + - events + - search + annotations: + readOnlyHint: true + idempotentHint: true + parameters: + - name: q + type: string + description: "Free text search query (searches title, description, location, attendees)" + examples: ["John Smith", "project Alpha", "conference room", "standup meeting"] + - name: calendar_id + type: string + description: "Calendar to search ('primary' or specific calendar ID)" + default: "primary" + examples: ["primary", "work@company.com", "team-calendar@company.com"] + - name: time_min + type: string + format: date-time + description: "Earliest event start time to include (RFC3339 format)" + default: null + examples: ["2024-01-15T00:00:00Z", "2024-01-15T09:00:00-08:00"] + - name: time_max + type: string + format: date-time + description: "Latest event start time to include (RFC3339 format)" + default: null + examples: ["2024-01-15T23:59:59Z", "2024-01-15T17:00:00-08:00"] + - name: max_results + type: integer + description: "Maximum number of events to return" + default: 250 + minimum: 1 + maximum: 2500 + examples: [10, 50, 250] + - name: page_token + type: string + description: "Token for pagination" + default: null + examples: ["CAESGjBpNDd2Nmp2Zml2cXRwYjBpOXA", "next_page_token_example"] + return: + type: object + description: "Search results with matching events" + properties: + events: + type: array + description: "Matching events" + items: + type: object + properties: + id: + type: string + description: "Event identifier" + summary: + type: string + description: "Event title" + start_time: + type: string + format: date-time + description: "Event start time" + end_time: + type: string + format: date-time + description: "Event end time" + location: + type: string + description: "Event location" + description: + type: string + description: "Event description" + htmlLink: + type: string + description: "Google Calendar web URL" + next_page_token: + type: string + description: "Token for next page of results" + total_results: + type: integer + description: "Number of results in current page" + required: ["events"] + language: python + source: + file: ../python/google_calendar_client.py diff --git a/skills/mxcp-expert/assets/project-templates/google-calendar/tools/whoami.yml b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/whoami.yml new file mode 100644 index 0000000..baa6893 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/google-calendar/tools/whoami.yml @@ -0,0 +1,53 @@ +mxcp: 1 +tool: + name: whoami + title: Current User Information + description: | + Get the current authenticated user's information (id, email, name) from Google OAuth context. + Use this tool to verify authentication status and get user profile data. + + Example usage: + - "Who am I logged in as?" + - "What's my Google account information?" + - "Show me my user profile" + tags: + - google-calendar + - user + - auth + annotations: + readOnlyHint: true + idempotentHint: true + parameters: [] + return: + type: object + description: Current user information from Google OAuth profile + properties: + id: + type: string + description: Google user ID (subject) + email: + type: string + description: User's email address + name: + type: string + description: User's full display name + given_name: + type: string + description: User's first name + family_name: + type: string + description: User's last name + picture: + type: string + description: User's profile picture URL + locale: + type: string + description: User's locale (e.g., 'en-US') + verified_email: + type: boolean + description: Whether email address is verified + required: ["id", "email", "name"] + language: python + source: + file: ../python/google_calendar_client.py + # NOTE: tests section omitted - OAuth tools cannot be tested via mxcp CLI diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/README.md b/skills/mxcp-expert/assets/project-templates/jira-oauth/README.md new file mode 100644 index 0000000..691d573 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/README.md @@ -0,0 +1,160 @@ +# Connect Jira to MXCP with OAuth + +This example shows how to connect JIRA to MXCP using secure OAuth authentication. + +## What You Get + +Once configured, you can query your Jira data directly from MXCP: + +```sql +-- Find all issues assigned to you +SELECT jql_query_jira('assignee = currentUser()') AS my_issues; + +-- Get recent bugs in a project +SELECT jql_query_jira('project = MYPROJECT AND type = Bug AND created >= -7d') AS recent_bugs; + +-- List all your accessible projects +SELECT list_projects_jira() AS projects; + +-- Get user information +SELECT get_user_jira('john.doe@company.com') AS user_info; +``` + +## Quick Setup Guide + +### Step 1: Create Your OAuth App in Atlassian + +1. Go to [Atlassian Developer Console](https://developer.atlassian.com/console/myapps/) +2. Click **Create** → **OAuth 2.0 (3LO)** +3. Fill in your app details: + - **App name**: `MXCP Jira Integration` (or whatever you prefer) + - **Description**: `OAuth integration for MXCP` +4. Click **Create** + +### Step 2: Configure OAuth Settings + +After creating your app: + +1. Click on your newly created app +2. Go to **Permissions** → **Add** → **Jira API** +3. Add these scopes: + - `read:me` (to read your own profile information) + - `read:jira-work` (to read issues and projects) + - `read:jira-user` (to read user information) + - `offline_access` (to refresh tokens) + +4. Go to **Authorization** → **OAuth 2.0 (3LO)** +5. Add your callback URL based on your deployment: + - **For production**: `https://your-domain.com/atlassian/callback` + - **For local development**: `http://localhost:8000/atlassian/callback` + - **For ngrok testing**: `https://your-ngrok-url.ngrok.io/atlassian/callback` + +6. **Important**: Save your **Client ID** and **Client Secret** - you'll need these next! + +### Step 3: Set Up Environment Variables + +Create a `.env` file or set these environment variables: + +```bash +export ATLASSIAN_CLIENT_ID="your-client-id-here" +export ATLASSIAN_CLIENT_SECRET="your-client-secret-here" +``` + +### Step 4: Configure MXCP + +This example includes a ready-to-use `config.yml` file that you can customize with your OAuth credentials. You can either: + +- **Use the included file**: Edit the existing `config.yml` in this directory +- **Create your own**: Use the template below + +Configuration template: + +```yaml +mxcp: 1.0.0 +transport: + http: + port: 8000 + host: 0.0.0.0 + # Set base_url to your server's public URL for production + base_url: http://localhost:8000 + +projects: + my-jira-project: + profiles: + dev: + # OAuth Configuration + auth: + provider: atlassian + clients: + - client_id: "${ATLASSIAN_CLIENT_ID}" + client_secret: "${ATLASSIAN_CLIENT_SECRET}" + name: "MXCP Jira Integration" + redirect_uris: + # For production, use your actual domain (must match base_url above) + - "https://your-domain.com/atlassian/callback" + # For local development, uncomment the line below: + # - "http://localhost:8000/atlassian/callback" + scopes: + - "mxcp:access" + atlassian: + client_id: "${ATLASSIAN_CLIENT_ID}" + client_secret: "${ATLASSIAN_CLIENT_SECRET}" + scope: "read:me read:jira-work read:jira-user offline_access" + callback_path: "/atlassian/callback" + auth_url: "https://auth.atlassian.com/authorize" + token_url: "https://auth.atlassian.com/oauth/token" + + # Plugin Configuration (minimal setup required!) + plugin: + config: + jira_oauth: {} # Named 'jira_oauth' here, but UDFs use 'jira' suffix from mxcp-site.yml +``` + +### Step 5: Install and Run + +1. **Install dependencies**: + ```bash + pip install atlassian-python-api requests + ``` + +2. **Start MXCP**: + ```bash + # From the examples/jira-oauth directory: + MXCP_CONFIG=config.yml mxcp serve + ``` + +3. **Authenticate**: + - Configure the MXCP server in your MCP client (e.g., Claude Desktop) + - When the client connects, you'll be redirected to Atlassian to authorize the app + - After authorization, you'll be redirected back to your MCP client + - You're now ready to query Jira! + +## Available Functions + +| Function | Description | Example | +|----------|-------------|---------| +| `jql_query_jira(query, start, limit)` | Execute JQL queries | `SELECT jql_query_jira('project = TEST')` | +| `list_projects_jira()` | List all your accessible projects | `SELECT list_projects_jira()` | +| `get_project_jira(key)` | Get details for a specific project | `SELECT get_project_jira('TEST')` | +| `get_user_jira(username)` | Get user information | `SELECT get_user_jira('john@company.com')` | + +## Example Queries + +```sql +-- Get your assigned issues +SELECT jql_query_jira('assignee = currentUser() AND status != Done', 0, 20) AS my_open_issues; + +-- Find high priority bugs +SELECT jql_query_jira('priority = High AND type = Bug', 0, 10) AS high_priority_bugs; + +-- Recent activity in a project +SELECT jql_query_jira('project = MYPROJECT AND updated >= -3d') AS recent_activity; + +-- Get project information +SELECT + list_projects_jira() AS all_projects, + get_project_jira('MYPROJECT') AS project_details; + +-- Find issues by reporter +SELECT jql_query_jira('reporter = "john.doe@company.com"') AS johns_issues; +``` diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/config.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/config.yml new file mode 100644 index 0000000..907b343 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/config.yml @@ -0,0 +1,36 @@ +mxcp: 1 +transport: + http: + port: 8000 + host: 0.0.0.0 + # Set base_url to your server's public URL for production + base_url: http://localhost:8000 + +projects: + jira-oauth-demo: + profiles: + dev: + # OAuth Authentication Configuration + auth: + provider: atlassian + clients: + - client_id: "${ATLASSIAN_CLIENT_ID}" + client_secret: "${ATLASSIAN_CLIENT_SECRET}" + name: "MXCP Jira OAuth Integration" + redirect_uris: + # For production, use your actual domain (must match base_url above) + - "http://localhost:8000/atlassian/callback" + scopes: + - "mxcp:access" + atlassian: + client_id: "${ATLASSIAN_CLIENT_ID}" + client_secret: "${ATLASSIAN_CLIENT_SECRET}" + scope: "read:me read:jira-work read:jira-user offline_access" + callback_path: "/atlassian/callback" + auth_url: "https://auth.atlassian.com/authorize" + token_url: "https://auth.atlassian.com/oauth/token" + + # Plugin Configuration (minimal configuration - uses OAuth context!) + plugin: + config: + jira_oauth: {} # Named 'jira_oauth' here, but UDFs use 'jira' suffix from mxcp-site.yml diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/mxcp-site.yml new file mode 100644 index 0000000..6c94e3e --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/mxcp-site.yml @@ -0,0 +1,8 @@ +mxcp: 1 +project: jira-oauth-demo +profile: dev + +plugin: + - name: jira + module: mxcp_plugin_jira_oauth + config: jira_oauth diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/__init__.py b/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/__init__.py new file mode 100644 index 0000000..0eafb06 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/__init__.py @@ -0,0 +1,10 @@ +""" +MXCP Jira OAuth Plugin + +This plugin provides UDFs for querying Atlassian Jira using OAuth authentication. +Unlike the API token version, this plugin uses OAuth tokens from authenticated users. +""" + +from .plugin import MXCPPlugin + +__all__ = ["MXCPPlugin"] diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/plugin.py b/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/plugin.py new file mode 100644 index 0000000..198a2cc --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/plugins/mxcp_plugin_jira_oauth/plugin.py @@ -0,0 +1,250 @@ +""" +Jira OAuth Plugin Implementation + +This module provides UDFs for querying Atlassian Jira using JQL with OAuth 2.0 authentication. +""" + +import json +import logging +from typing import Any, Dict, List, Optional + +import requests +from atlassian import Jira + +from mxcp.plugins import MXCPBasePlugin, udf +from mxcp.sdk.auth.context import get_user_context + +logger = logging.getLogger(__name__) + + +class MXCPPlugin(MXCPBasePlugin): + """Jira OAuth plugin that provides JQL query functionality using OAuth 2.0 Bearer tokens.""" + + def __init__(self, config: Dict[str, Any]): + """Initialize the Jira OAuth plugin. + + Args: + config: Plugin configuration containing optional settings + Optional keys: + - oauth_token: Fallback OAuth Bearer token (if not using user context) + """ + super().__init__(config) + self.fallback_oauth_token = config.get("oauth_token", "") + self.instance_url: Optional[str] = None + + def _get_oauth_token(self) -> str: + """Get OAuth token from user context or fallback configuration. + + Returns: + OAuth Bearer token + + Raises: + ValueError: If no OAuth token is available + """ + # First try to get token from user context (preferred) + user_context = get_user_context() + if user_context and user_context.external_token: + logger.debug("Using OAuth token from user context") + return user_context.external_token + + # Fall back to configured token + if self.fallback_oauth_token: + logger.debug("Using fallback OAuth token from configuration") + return self.fallback_oauth_token + + raise ValueError("No OAuth token available from user context or configuration") + + def _get_cloud_id_and_url(self, oauth_token: str) -> tuple[str, str]: + """Get the cloud ID and instance URL for the first accessible Jira instance using the OAuth token. + + Args: + oauth_token: OAuth Bearer token + + Returns: + Tuple of (cloud_id, instance_url) for the first accessible Jira instance + + Raises: + ValueError: If cloud ID and URL cannot be retrieved + """ + try: + response = requests.get( + "https://api.atlassian.com/oauth/token/accessible-resources", + headers={"Authorization": f"Bearer {oauth_token}", "Accept": "application/json"}, + ) + response.raise_for_status() + + resources = response.json() + logger.debug(f"Found {len(resources)} accessible resources") + + # Use the first accessible resource + if resources: + cloud_id = resources[0].get("id") + instance_url = resources[0].get("url") + logger.info(f"Using cloud ID: {cloud_id} for instance: {instance_url}") + return cloud_id, instance_url + + raise ValueError(f"No accessible resources found for OAuth token") + + except requests.RequestException as e: + logger.error(f"Failed to get cloud ID and URL: {e}") + raise ValueError(f"Failed to retrieve cloud ID and URL: {e}") + + def _create_jira_client(self) -> Jira: + """Create a Jira client with OAuth authentication using the correct API gateway URL. + + Returns: + Configured Jira client instance + """ + oauth_token = self._get_oauth_token() + + # Get the cloud ID and instance URL for the first accessible Jira instance + cloud_id, instance_url = self._get_cloud_id_and_url(oauth_token) + + # Store the instance URL for constructing web UI URLs + self.instance_url = instance_url + + # Construct the API gateway URL for OAuth requests + api_gateway_url = f"https://api.atlassian.com/ex/jira/{cloud_id}" + logger.info("API Gateway URL: %s", api_gateway_url) + + # Create a requests session with OAuth Bearer token + session = requests.Session() + session.headers["Authorization"] = f"Bearer {oauth_token}" + + # Create and return Jira client with the OAuth session and API gateway URL + # Explicitly set cloud=True since we're using Jira Cloud with OAuth + return Jira(url=api_gateway_url, session=session, cloud=True) + + @udf + def jql_query(self, query: str, start: Optional[int] = 0, limit: Optional[int] = None) -> str: + """Execute a JQL query against Jira using OAuth authentication. + + Args: + query: The JQL query string + start: Starting index for pagination (default: 0) + limit: Maximum number of results to return (default: None, meaning no limit) + + Returns: + JSON string containing Jira issues matching the query + """ + logger.info( + "Executing JQL query with OAuth: %s with start=%s, limit=%s", query, start, limit + ) + + # Create Jira client with current user's OAuth token + jira = self._create_jira_client() + + raw = jira.jql( + jql=query, + start=start, + limit=limit, + fields=( + "key,summary,status,resolution,resolutiondate," + "assignee,reporter,issuetype,priority," + "created,updated,labels,fixVersions,parent" + ), + ) + + def _name(obj: Optional[Dict[str, Any]]) -> Optional[str]: + """Return obj['name'] if present, else None.""" + return obj.get("name") if obj else None + + def _key(obj: Optional[Dict[str, Any]]) -> Optional[str]: + return obj.get("key") if obj else None + + cleaned: List[Dict[str, Any]] = [] + for issue in raw.get("issues", []): + f = issue["fields"] + + cleaned.append( + { + "key": issue["key"], + "summary": f.get("summary"), + "status": _name(f.get("status")), + "resolution": _name(f.get("resolution")), + "resolution_date": f.get("resolutiondate"), + "assignee": _name(f.get("assignee")), + "reporter": _name(f.get("reporter")), + "type": _name(f.get("issuetype")), + "priority": _name(f.get("priority")), + "created": f.get("created"), + "updated": f.get("updated"), + "labels": f.get("labels") or [], + "fix_versions": [_name(v) for v in f.get("fixVersions", [])], + "parent": _key(f.get("parent")), + "url": f"{self.instance_url}/browse/{issue['key']}", # web UI URL + } + ) + + return json.dumps(cleaned) + + @udf + def get_user(self, username: str) -> str: + """Get details for a specific user by username using OAuth. + + Args: + username: The username to search for + + Returns: + JSON string containing the user details + """ + logger.info("Getting user details with OAuth for username: %s", username) + + # Create Jira client with current user's OAuth token + jira = self._create_jira_client() + + return json.dumps(jira.user_find_by_user_string(query=username)) + + @udf + def list_projects(self) -> str: + """List all accessible Jira projects using OAuth authentication. + + Returns: + JSON string containing an array of accessible Jira projects + """ + logger.info("Listing all projects with OAuth") + + # Create Jira client with current user's OAuth token + jira = self._create_jira_client() + + raw_projects: List[Dict[str, Any]] = jira.projects() + + def safe_name(obj: Optional[Dict[str, Any]]) -> Optional[str]: + return obj.get("displayName") or obj.get("name") if obj else None + + concise: List[Dict[str, Any]] = [] + for p in raw_projects: + concise.append( + { + "key": p.get("key"), + "name": p.get("name"), + "type": p.get("projectTypeKey"), # e.g. software, business + "lead": safe_name(p.get("lead")), + "url": f"{self.instance_url}/projects/{p.get('key')}", # web UI URL + } + ) + + return json.dumps(concise) + + @udf + def get_project(self, project_key: str) -> str: + """Get details for a specific project by its key using OAuth. + + Args: + project_key: The project key (e.g., 'TEST' for project TEST) + + Returns: + JSON string containing the project details + """ + logger.info("Getting project details with OAuth for key: %s", project_key) + + # Create Jira client with current user's OAuth token + jira = self._create_jira_client() + + info = jira.project(project_key) + # remove the self key if it exists + if "self" in info: + info.pop("self") + # Add web UI URL + info["url"] = f"{self.instance_url}/projects/{project_key}" + return json.dumps(info) diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_current_user.sql b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_current_user.sql new file mode 100644 index 0000000..e8c3cc5 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_current_user.sql @@ -0,0 +1,2 @@ +-- Get the username of the currently authenticated user +SELECT get_username() as authenticated_user; \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_project.sql b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_project.sql new file mode 100644 index 0000000..81c8c33 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_project.sql @@ -0,0 +1,2 @@ +-- Get details for a specific Jira project using OAuth authentication +SELECT get_project_jira($project_key) as result; diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_user.sql b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_user.sql new file mode 100644 index 0000000..17864e3 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/get_user.sql @@ -0,0 +1,2 @@ +-- Get details for a specific Jira user using OAuth authentication +SELECT get_user_jira($username) as result; diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/jql.sql b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/jql.sql new file mode 100644 index 0000000..aee75cb --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/jql.sql @@ -0,0 +1,2 @@ +-- Example JQL query endpoint using OAuth authentication +SELECT jql_query_jira($query, $start, $limit) as result; diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/list_projects.sql b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/list_projects.sql new file mode 100644 index 0000000..a5d0fc0 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/sql/list_projects.sql @@ -0,0 +1,2 @@ +-- List all projects in Jira using OAuth authentication +SELECT list_projects_jira() as result; diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_current_user.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_current_user.yml new file mode 100644 index 0000000..b28e476 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_current_user.yml @@ -0,0 +1,25 @@ +mxcp: 1 + +tool: + name: get_current_user + description: | + Get the username of the currently authenticated user in MXCP. + This tool returns the username of the person who is authenticated via OAuth with Jira. + It's useful for understanding whose credentials are being used for Jira API calls, + and can help verify that the OAuth authentication flow completed successfully. + The username typically corresponds to the Atlassian account email address. + type: tool + annotations: + title: Get Current Authenticated User + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: false + return: + type: string + description: | + The username (typically email address) of the currently authenticated user. + Returns NULL if no user is authenticated. + language: "sql" + source: + file: "../sql/get_current_user.sql" diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_project.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_project.yml new file mode 100644 index 0000000..e1fdb02 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_project.yml @@ -0,0 +1,32 @@ +mxcp: 1 + +tool: + name: get_project + description: | + Get details for a specific project in your Jira instance by its project key using OAuth authentication. + Returns a JSON string containing the project's details. + type: tool + annotations: + title: Get Project Details (OAuth) + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: project_key + type: string + description: | + The project key to search for. This is the short identifier for the project (e.g., 'TEST' for project TEST). + Project keys are typically uppercase and contain only letters and numbers. + examples: [ + "TEST", + "PROJ", + "DEV" + ] + return: + type: string + description: | + A JSON string containing the project's details. + language: "sql" + source: + file: "../sql/get_project.sql" diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_user.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_user.yml new file mode 100644 index 0000000..0756215 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/get_user.yml @@ -0,0 +1,30 @@ +mxcp: 1 + +tool: + name: get_user + description: | + Get details for a specific user in your Jira instance by their username using OAuth authentication. + Returns a JSON string containing the user's details. + type: tool + annotations: + title: Get User Details (OAuth) + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: username + type: string + description: | + The username to search for. This is typically the user's email address or username in Jira. + examples: [ + "john.doe@example.com", + "jane.smith" + ] + return: + type: string + description: | + A JSON string containing the user's details. + language: "sql" + source: + file: "../sql/get_user.sql" diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/jql.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/jql.yml new file mode 100644 index 0000000..fd6a0de --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/jql.yml @@ -0,0 +1,50 @@ +mxcp: 1 + +tool: + name: jql + description: | + Execute a JQL (Jira Query Language) query to search for issues in your Jira instance using OAuth authentication. + Returns a JSON string containing the matching issues with their details. + Use the start and limit parameters to paginate through large result sets. + type: tool + annotations: + title: JQL Query (OAuth) + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + parameters: + - name: query + type: string + description: | + The JQL query string to execute. Examples: + - "project = TEST" to find all issues in the TEST project + - "assignee = currentUser()" to find issues assigned to you + - "status = 'In Progress'" to find issues in progress + examples: [ + "project = TEST", + "status = 'In Progress'", + "project = TEST AND status = 'Done'", + "created >= -30d ORDER BY created DESC" + ] + - name: start + type: integer + description: | + The index of the first result to return (0-based). + Use this for pagination: start=0 for first page, start=50 for second page, etc. + Defaults to 0 if not specified. + examples: [0, 50, 100] + - name: limit + type: integer + description: | + Maximum number of results to return. + If not specified, returns all matching results. + Recommended to use with start parameter for pagination. + examples: [50, 100, 200] + return: + type: string + description: | + A JSON string containing an array of Jira issues. + language: "sql" + source: + file: "../sql/jql.sql" diff --git a/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/list_projects.yml b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/list_projects.yml new file mode 100644 index 0000000..90c0702 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira-oauth/tools/list_projects.yml @@ -0,0 +1,21 @@ +mxcp: 1 + +tool: + name: list_projects + description: | + List all projects in your Jira instance using OAuth authentication. + Returns a JSON string containing an array of projects with their details. + type: tool + annotations: + title: List Projects (OAuth) + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + return: + type: string + description: | + A JSON string containing an array of Jira projects. + language: "sql" + source: + file: "../sql/list_projects.sql" diff --git a/skills/mxcp-expert/assets/project-templates/jira/README.md b/skills/mxcp-expert/assets/project-templates/jira/README.md new file mode 100644 index 0000000..029fb87 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/README.md @@ -0,0 +1,145 @@ +# MXCP Jira Python Endpoints Example + +This example demonstrates how to use MXCP with Jira data using Python endpoints. This approach uses Python functions directly as MCP tools. + +## Overview + +This example provides Python MCP endpoints that allow you to: +- Execute JQL queries to search issues +- Get detailed information for specific issues +- Get user information +- List projects and their details +- Get project metadata + +## Implementation Approach + +This example uses Python functions that are exposed as MCP tools: +- Python functions handle the Jira API interactions +- Tool definitions map to these Python functions +- Results are returned as JSON data + +## Configuration + +### 1. Creating an Atlassian API Token + +**Important:** This plugin currently only supports API tokens **without scopes**. While Atlassian has introduced scoped API tokens, there are known compatibility issues when using scoped tokens with basic authentication that this plugin relies on. + +To create an API token without scopes: + +1. **Log in to your Atlassian account** at [https://id.atlassian.com/manage-profile/security/api-tokens](https://id.atlassian.com/manage-profile/security/api-tokens) + +2. **Verify your identity** (if prompted): + - Atlassian may ask you to verify your identity before creating API tokens + - Check your email for a one-time passcode and enter it when prompted + +3. **Create the API token**: + - Click **"Create API token"** (not "Create API token with scopes") + - Enter a descriptive name for your token (e.g., "MXCP Jira Python Integration") + - Select an expiration date (tokens can last from 1 day to 1 year) + - Click **"Create"** + +4. **Copy and save your token**: + - Click **"Copy to clipboard"** to copy the token + - **Important:** Save this token securely (like in a password manager) as you won't be able to view it again + - This token will be used as your "password" in the configuration below + +### 2. User Configuration + +Add the following to your MXCP user config (`~/.mxcp/config.yml`): + +```yaml +mxcp: 1 + +projects: + jira-demo: + profiles: + default: + secrets: + - name: "jira" + type: "python" + parameters: + url: "https://your-domain.atlassian.net" + username: "your-email@example.com" + password: "your-api-token" # Use the API token you created above +``` + +### 3. Site Configuration + +Create an `mxcp-site.yml` file: + +```yaml +mxcp: 1 +project: jira-demo +profile: default +secrets: + - jira +``` + +## Available Tools + +### JQL Query +Execute JQL queries: +```bash +mxcp run tool jql_query --param query="project = TEST" --param limit=10 +``` + +### Get Issue +Get detailed information for a specific issue by its key: +```bash +mxcp run tool get_issue --param issue_key="RD-123" +``` + +### Get User +Get a specific user by their account ID: +```bash +mxcp run tool get_user --param account_id="557058:ab168c94-8485-405c-88e6-6458375eb30b" +``` + +### Search Users +Search for users by name, email, or other criteria: +```bash +mxcp run tool search_user --param query="john.doe@example.com" +``` + +### List Projects +List all projects: +```bash +mxcp run tool list_projects +``` + +### Get Project +Get project details: +```bash +mxcp run tool get_project --param project_key="TEST" +``` + +### Get Project Roles +Get all roles available in a project: +```bash +mxcp run tool get_project_roles --param project_key="TEST" +``` + +### Get Project Role Users +Get users and groups for a specific role in a project: +```bash +mxcp run tool get_project_role_users --param project_key="TEST" --param role_name="Developers" +``` + +## Project Structure + +``` +jira-python/ +├── mxcp-site.yml # Site configuration +├── python/ # Python implementations +│ └── jira_endpoints.py # All JIRA endpoint functions +├── tools/ # Tool definitions +│ ├── jql_query.yml +│ ├── get_issue.yml +│ ├── get_user.yml +│ ├── search_user.yml +│ ├── list_projects.yml +│ ├── get_project.yml +│ ├── get_project_roles.yml +│ └── get_project_role_users.yml +└── README.md +``` \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/jira/config.yml b/skills/mxcp-expert/assets/project-templates/jira/config.yml new file mode 100644 index 0000000..e81a555 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/config.yml @@ -0,0 +1,17 @@ +mxcp: 1 + +# Sample configuration file for JIRA Python endpoints example +# Copy this to ~/.mxcp/config.yml and update with your JIRA details + +projects: + jira-demo: + profiles: + default: + secrets: + - name: "jira" + type: "python" + parameters: + url: "${JIRA_URL}" + username: "${JIRA_USERNAME}" + password: "${JIRA_API_TOKEN}" + diff --git a/skills/mxcp-expert/assets/project-templates/jira/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/jira/mxcp-site.yml new file mode 100644 index 0000000..581f427 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/mxcp-site.yml @@ -0,0 +1,5 @@ +mxcp: 1 +project: jira-demo +profile: default +secrets: + - jira diff --git a/skills/mxcp-expert/assets/project-templates/jira/python/jira_endpoints.py b/skills/mxcp-expert/assets/project-templates/jira/python/jira_endpoints.py new file mode 100644 index 0000000..c0aa6ad --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/python/jira_endpoints.py @@ -0,0 +1,569 @@ +""" +JIRA Python Endpoints + +This module provides direct Python MCP endpoints for querying Atlassian JIRA. +This is a simpler alternative to the plugin-based approach. +""" + +from typing import Dict, Any, List, Optional, Callable +import logging +from atlassian import Jira +from mxcp.runtime import config, on_init, on_shutdown +import threading +import functools +import time + +logger = logging.getLogger(__name__) + +# Global JIRA client for reuse across all function calls +jira_client: Optional[Jira] = None +# Thread lock to protect client initialization +_client_lock = threading.Lock() + + +@on_init +def setup_jira_client() -> None: + """Initialize JIRA client when server starts. + + Thread-safe: multiple threads can safely call this simultaneously. + """ + global jira_client + + with _client_lock: + logger.info("Initializing JIRA client...") + + jira_config = config.get_secret("jira") + if not jira_config: + raise ValueError( + "JIRA configuration not found. Please configure JIRA secrets in your user config." + ) + + required_keys = ["url", "username", "password"] + missing_keys = [key for key in required_keys if not jira_config.get(key)] + if missing_keys: + raise ValueError(f"Missing JIRA configuration keys: {', '.join(missing_keys)}") + + jira_client = Jira( + url=jira_config["url"], + username=jira_config["username"], + password=jira_config["password"], + cloud=True, + ) + + logger.info("JIRA client initialized successfully") + + +@on_shutdown +def cleanup_jira_client() -> None: + """Clean up JIRA client when server stops.""" + global jira_client + if jira_client: + # JIRA client doesn't need explicit cleanup, but we'll clear the reference + jira_client = None + logger.info("JIRA client cleaned up") + + +def retry_on_session_expiration(func: Callable[..., Any]) -> Callable[..., Any]: + """ + Decorator that automatically retries functions on JIRA session expiration. + + This only retries on HTTP 401 Unauthorized errors, not other authentication failures. + Retries up to 2 times on session expiration (3 total attempts). + Thread-safe: setup_jira_client() handles concurrent access internally. + + Usage: + @retry_on_session_expiration + def my_jira_function(): + # Function that might fail due to session expiration + pass + """ + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + max_retries = 2 # Hardcoded: 2 retries = 3 total attempts + + for attempt in range(max_retries + 1): + try: + return func(*args, **kwargs) + except Exception as e: + # Check if this is a 401 Unauthorized error (session expired) + if _is_session_expired(e): + if attempt < max_retries: + logger.warning( + f"Session expired on attempt {attempt + 1} in {func.__name__}: {e}" + ) + logger.info( + f"Retrying after re-initializing client (attempt {attempt + 2}/{max_retries + 1})" + ) + + try: + setup_jira_client() # Thread-safe internally + time.sleep(0.1) # Small delay to avoid immediate retry + except Exception as setup_error: + logger.error(f"Failed to re-initialize JIRA client: {setup_error}") + raise setup_error # Raise the setup error, not the original session error + else: + # Last attempt failed, re-raise the session expiration error + raise e + else: + # Not a session expiration error, re-raise immediately + raise e + + return wrapper + + +def _is_session_expired(exception: Exception) -> bool: + """Check if the exception indicates a JIRA session has expired.""" + error_msg = str(exception).lower() + + # Check for HTTP 401 Unauthorized + if "401" in error_msg or "unauthorized" in error_msg: + return True + + # Check for common session expiration messages + if any( + phrase in error_msg + for phrase in [ + "session expired", + "session invalid", + "authentication failed", + "invalid session", + "session timeout", + ] + ): + return True + + return False + + +def _get_jira_client() -> Jira: + """Get the global JIRA client.""" + if jira_client is None: + raise RuntimeError("JIRA client not initialized. Make sure the server is started properly.") + return jira_client + + +@retry_on_session_expiration +def jql_query( + query: str, start: Optional[int] = None, limit: Optional[int] = None +) -> List[Dict[str, Any]]: + """Execute a JQL query against Jira. + + Args: + query: The JQL query string + start: Starting index for pagination (default: None, which becomes 0) + limit: Maximum number of results to return (default: None, meaning no limit) + + Returns: + List of Jira issues matching the query + """ + logger.info("Executing JQL query: %s with start=%s, limit=%s", query, start, limit) + + jira = _get_jira_client() + + raw = jira.jql( + jql=query, + start=start if start is not None else 0, + limit=limit, + fields=( + "key,summary,status,resolution,resolutiondate," + "assignee,reporter,issuetype,priority," + "created,updated,labels,fixVersions,parent" + ), + ) + + if not raw: + raise ValueError("JIRA JQL query returned empty result") + + def _name(obj: Optional[Dict[str, Any]]) -> Optional[str]: + """Return obj['name'] if present, else None.""" + return obj.get("name") if obj else None + + def _key(obj: Optional[Dict[str, Any]]) -> Optional[str]: + return obj.get("key") if obj else None + + cleaned: List[Dict[str, Any]] = [] + jira_url = jira.url + + for issue in raw.get("issues", []): + f = issue["fields"] + + cleaned.append( + { + "key": issue["key"], + "summary": f.get("summary"), + "status": _name(f.get("status")), + "resolution": _name(f.get("resolution")), + "resolution_date": f.get("resolutiondate"), + "assignee": _name(f.get("assignee")), + "reporter": _name(f.get("reporter")), + "type": _name(f.get("issuetype")), + "priority": _name(f.get("priority")), + "created": f.get("created"), + "updated": f.get("updated"), + "labels": f.get("labels") or [], + "fix_versions": [_name(v) for v in f.get("fixVersions", [])], + "parent": _key(f.get("parent")), + "url": f"{jira_url}/browse/{issue['key']}", # web UI URL + } + ) + + return cleaned + + +@retry_on_session_expiration +def get_issue(issue_key: str) -> Dict[str, Any]: + """Get detailed information for a specific JIRA issue by its key. + + Args: + issue_key: The issue key (e.g., 'RD-123', 'TEST-456') + + Returns: + Dictionary containing comprehensive issue information + + Raises: + ValueError: If issue is not found or access is denied + """ + logger.info("Getting issue details for key: %s", issue_key) + jira = _get_jira_client() + + # Get issue by key - this method handles the REST API call + issue = jira.issue(issue_key) + + # Extract and clean up the most important fields for easier consumption + fields = issue.get("fields", {}) + jira_url = jira.url + + def _safe_get(obj: Any, key: str, default: Any = None) -> Any: + """Safely get a value from a dict/object that might be None.""" + if obj is None: + return default + if isinstance(obj, dict): + return obj.get(key, default) + return getattr(obj, key, default) + + cleaned_issue = { + "key": issue.get("key"), + "id": issue.get("id"), + "summary": fields.get("summary"), + "description": fields.get("description"), + "status": _safe_get(fields.get("status"), "name"), + "assignee": _safe_get(fields.get("assignee"), "displayName"), + "assignee_account_id": _safe_get(fields.get("assignee"), "accountId"), + "reporter": _safe_get(fields.get("reporter"), "displayName"), + "reporter_account_id": _safe_get(fields.get("reporter"), "accountId"), + "issue_type": _safe_get(fields.get("issuetype"), "name"), + "priority": _safe_get(fields.get("priority"), "name"), + "resolution": _safe_get(fields.get("resolution"), "name"), + "resolution_date": fields.get("resolutiondate"), + "created": fields.get("created"), + "updated": fields.get("updated"), + "due_date": fields.get("duedate"), + "labels": fields.get("labels", []) or [], + "components": ( + [comp.get("name") for comp in fields.get("components", []) if comp and comp.get("name")] + if fields.get("components") + else [] + ), + "fix_versions": ( + [ver.get("name") for ver in fields.get("fixVersions", []) if ver and ver.get("name")] + if fields.get("fixVersions") + else [] + ), + "project": { + "key": _safe_get(fields.get("project"), "key"), + "name": _safe_get(fields.get("project"), "name"), + }, + "parent": _safe_get(fields.get("parent"), "key"), + "url": f"{jira_url}/browse/{issue.get('key')}", + } + + return cleaned_issue + + +@retry_on_session_expiration +def get_user(account_id: str) -> Dict[str, Any]: + """Get a specific user by their unique account ID. + + Args: + account_id: The unique Atlassian account ID for the user. + Example: "557058:ab168c94-8485-405c-88e6-6458375eb30b" + + Returns: + Dictionary containing filtered user details + + Raises: + ValueError: If user is not found or account ID is invalid + """ + logger.info("Getting user details for account ID: %s", account_id) + jira = _get_jira_client() + + # Get user by account ID - pass as account_id parameter for Jira Cloud + user = jira.user(account_id=account_id) + + # Return only the requested fields + return { + "accountId": user.get("accountId"), + "displayName": user.get("displayName"), + "emailAddress": user.get("emailAddress"), + "active": user.get("active"), + "timeZone": user.get("timeZone"), + } + + +@retry_on_session_expiration +def search_user(query: str) -> List[Dict[str, Any]]: + """Search for users by query string (username, email, or display name). + + Args: + query: Search term - can be username, email, display name, or partial matches. + Examples: "ben@raw-labs.com", "Benjamin Gaidioz", "ben", "benjamin", "gaidioz" + + Returns: + List of matching users with filtered fields. Empty list if no matches found. + """ + logger.info("Searching for users with query: %s", query) + jira = _get_jira_client() + + # user_find_by_user_string returns a list of users matching the query + users = jira.user_find_by_user_string(query=query) + + if not users: + return [] + + # Filter users to only include relevant fields + filtered_users = [] + for user in users: + filtered_users.append( + { + "accountId": user.get("accountId"), + "displayName": user.get("displayName"), + "emailAddress": user.get("emailAddress"), + "active": user.get("active"), + "timeZone": user.get("timeZone"), + } + ) + + return filtered_users + + +@retry_on_session_expiration +def list_projects() -> List[Dict[str, Any]]: + """Return a concise list of Jira projects. + + Returns: + List of dictionaries containing project information + """ + logger.info("Listing all projects") + + jira = _get_jira_client() + raw_projects: List[Dict[str, Any]] = jira.projects(expand="lead") + + def safe_name(obj: Optional[Dict[str, Any]]) -> Optional[str]: + return obj.get("displayName") or obj.get("name") if obj else None + + concise: List[Dict[str, Any]] = [] + jira_url = jira.url + + for p in raw_projects: + concise.append( + { + "key": p.get("key"), + "name": p.get("name"), + "type": p.get("projectTypeKey"), # e.g. software, business + "lead": safe_name(p.get("lead")), + "url": f"{jira_url}/projects/{p.get('key')}", # web UI URL + } + ) + + return concise + + +@retry_on_session_expiration +def get_project(project_key: str) -> Dict[str, Any]: + """Get details for a specific project by its key. + + Args: + project_key: The project key (e.g., 'TEST' for project TEST) + + Returns: + Dictionary containing the project details + + Raises: + ValueError: If project is not found or access is denied + """ + logger.info("Getting project details for key: %s", project_key) + jira = _get_jira_client() + + try: + info = jira.project(project_key) + except Exception as e: + # Handle various possible errors from the JIRA API + error_msg = str(e).lower() + if "404" in error_msg or "not found" in error_msg: + raise ValueError(f"Project '{project_key}' not found in JIRA") + elif "403" in error_msg or "forbidden" in error_msg: + raise ValueError(f"Access denied to project '{project_key}' in JIRA") + else: + # Re-raise other errors with context + raise ValueError(f"Error retrieving project '{project_key}': {e}") from e + + # Filter to essential fields only to avoid response size issues + cleaned_info = { + "key": info.get("key"), + "name": info.get("name"), + "description": info.get("description"), + "projectTypeKey": info.get("projectTypeKey"), + "simplified": info.get("simplified"), + "style": info.get("style"), + "isPrivate": info.get("isPrivate"), + "archived": info.get("archived"), + } + + # Add lead info if present + if "lead" in info and info["lead"]: + cleaned_info["lead"] = { + "displayName": info["lead"].get("displayName"), + "emailAddress": info["lead"].get("emailAddress"), + "accountId": info["lead"].get("accountId"), + "active": info["lead"].get("active"), + } + + cleaned_info["url"] = f"{jira.url}/projects/{project_key}" + + return cleaned_info + + +@retry_on_session_expiration +def get_project_roles(project_key: str) -> List[Dict[str, Any]]: + """Get all roles available in a project. + + Args: + project_key: The project key (e.g., 'TEST' for project TEST) + + Returns: + List of roles available in the project + + Raises: + ValueError: If project is not found or access is denied + """ + logger.info("Getting project roles for key: %s", project_key) + jira = _get_jira_client() + + try: + # Get all project roles using the correct method + project_roles = jira.get_project_roles(project_key) + + result = [] + for role_name, role_url in project_roles.items(): + # Extract role ID from URL (e.g., "https://domain.atlassian.net/rest/api/3/project/10000/role/10002") + role_id = role_url.split("/")[-1] + + result.append({"name": role_name, "id": role_id}) + + return result + + except Exception as e: + # Handle various possible errors from the JIRA API + error_msg = str(e).lower() + if "404" in error_msg or "not found" in error_msg: + raise ValueError(f"Project '{project_key}' not found in JIRA") + elif "403" in error_msg or "forbidden" in error_msg: + raise ValueError(f"Access denied to project '{project_key}' in JIRA") + else: + # Re-raise other errors with context + raise ValueError(f"Error retrieving project roles for '{project_key}': {e}") from e + + +@retry_on_session_expiration +def get_project_role_users(project_key: str, role_name: str) -> Dict[str, Any]: + """Get users and groups for a specific role in a project. + + Args: + project_key: The project key (e.g., 'TEST' for project TEST) + role_name: The name of the role to get users for + + Returns: + Dictionary containing users and groups for the specified role + + Raises: + ValueError: If project or role is not found, or access is denied + """ + logger.info("Getting users for role '%s' in project '%s'", role_name, project_key) + jira = _get_jira_client() + + try: + # First get all project roles to find the role ID + project_roles = jira.get_project_roles(project_key) + + if role_name not in project_roles: + available_roles = list(project_roles.keys()) + raise ValueError( + f"Role '{role_name}' not found in project '{project_key}'. Available roles: {available_roles}" + ) + + # Extract role ID from URL + role_url = project_roles[role_name] + role_id = role_url.split("/")[-1] + + # Get role details including actors (users and groups) + role_details = jira.get_project_actors_for_role_project(project_key, role_id) + + result = { + "project_key": project_key, + "role_name": role_name, + "role_id": role_id, + "users": [], + "groups": [], + } + + # Process actors (role_details is a list of actors) + if isinstance(role_details, list): + for actor in role_details: + if isinstance(actor, dict): + actor_type = actor.get("type", "") + if actor_type == "atlassian-user-role-actor": + # Individual user + user_info = { + "accountId": actor.get("actorUser", {}).get("accountId"), + "displayName": actor.get("displayName"), + } + result["users"].append(user_info) + elif actor_type == "atlassian-group-role-actor": + # Group + group_info = { + "name": actor.get("displayName"), + "groupId": actor.get("actorGroup", {}).get("groupId"), + } + result["groups"].append(group_info) + else: + # Handle other actor types or simple user entries + display_name = actor.get("displayName") or actor.get("name") + if display_name: + user_info = { + "accountId": actor.get("accountId"), + "displayName": display_name, + } + result["users"].append(user_info) + + return result + + except ValueError: + # Re-raise ValueError as-is (these are our custom error messages) + raise + except Exception as e: + # Handle various possible errors from the JIRA API + error_msg = str(e).lower() + + # Don't handle 401 errors here - let the retry decorator handle them + if "401" in error_msg or "unauthorized" in error_msg: + raise e # Let the retry decorator catch this + elif "404" in error_msg or "not found" in error_msg: + raise ValueError(f"Project '{project_key}' not found in JIRA") + elif "403" in error_msg or "forbidden" in error_msg: + raise ValueError(f"Access denied to project '{project_key}' in JIRA") + else: + # Re-raise other errors with context + raise ValueError( + f"Error retrieving users for role '{role_name}' in project '{project_key}': {e}" + ) from e diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/get_issue.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/get_issue.yml new file mode 100644 index 0000000..7d115e7 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/get_issue.yml @@ -0,0 +1,114 @@ +mxcp: 1 + +tool: + name: get_issue + description: | + Get detailed information for a specific JIRA issue by its key. + Returns comprehensive issue information including all fields, assignee, reporter, etc. + type: tool + annotations: + title: Get Issue + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: issue_key + type: string + description: | + The issue key (e.g., 'RD-123', 'TEST-456'). + This is the unique identifier for the issue visible in the Jira UI. + examples: [ + "RD-123", + "TEST-456", + "PROJ-789" + ] + return: + type: object + properties: + key: + type: string + description: The issue key + id: + type: string + description: The issue ID + summary: + type: string + description: The issue summary + description: + type: string + description: The issue description + status: + type: string + description: The current status + assignee: + type: string + description: The assignee display name + assignee_account_id: + type: string + description: The assignee account ID + reporter: + type: string + description: The reporter display name + reporter_account_id: + type: string + description: The reporter account ID + issue_type: + type: string + description: The issue type + priority: + type: string + description: The priority level + resolution: + type: string + description: The resolution + resolution_date: + type: string + description: The resolution date + created: + type: string + description: The creation date + updated: + type: string + description: The last update date + due_date: + type: string + description: The due date + labels: + type: array + items: + type: string + description: The issue labels + components: + type: array + items: + type: string + description: The issue components + fix_versions: + type: array + items: + type: string + description: The fix versions + project: + type: object + properties: + key: + type: string + name: + type: string + description: The project information + parent: + type: string + description: The parent issue key + url: + type: string + description: The issue URL + tests: + - name: "Get issue by key" + description: "Verify issue retrieval returns expected structure" + arguments: + - key: issue_key + value: "RD-15333" diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/get_project.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project.yml new file mode 100644 index 0000000..282d5fe --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project.yml @@ -0,0 +1,76 @@ +mxcp: 1 + +tool: + name: get_project + description: | + Get details for a specific project by its key. + Returns comprehensive project information including description, settings, and lead. + type: tool + annotations: + title: Get Project + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: project_key + type: string + description: | + The project key (e.g., 'TEST' for project TEST). + This is the short identifier for the project. + examples: [ + "TEST", + "PROJ", + "DEV" + ] + return: + type: object + properties: + key: + type: string + description: The project key + name: + type: string + description: The project name + description: + type: string + description: The project description + projectTypeKey: + type: string + description: The project type key + simplified: + type: boolean + description: Whether the project is simplified + style: + type: string + description: The project style + isPrivate: + type: boolean + description: Whether the project is private + archived: + type: boolean + description: Whether the project is archived + lead: + type: object + properties: + displayName: + type: string + emailAddress: + type: string + accountId: + type: string + active: + type: boolean + description: The project lead information + url: + type: string + description: The project URL + tests: + - name: "Get project by key" + description: "Verify project retrieval returns expected structure" + arguments: + - key: project_key + value: "RD" diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_role_users.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_role_users.yml new file mode 100644 index 0000000..f821917 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_role_users.yml @@ -0,0 +1,78 @@ +mxcp: 1 + +tool: + name: get_project_role_users + description: | + Get users and groups for a specific role in a project. + Returns detailed information about users and groups assigned to the role. + type: tool + annotations: + title: Get Project Role Users + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: project_key + type: string + description: | + The project key (e.g., 'TEST' for project TEST). + This is the short identifier for the project. + examples: [ + "TEST", + "PROJ", + "DEV" + ] + - name: role_name + type: string + description: | + The name of the role to get users for. + Common roles include 'Administrators', 'Developers', 'Users'. + examples: [ + "Administrators", + "Developers", + "Users" + ] + return: + type: object + properties: + project_key: + type: string + description: The project key + role_name: + type: string + description: The role name + role_id: + type: string + description: The role ID + users: + type: array + items: + type: object + properties: + accountId: + type: string + displayName: + type: string + description: List of users in the role + groups: + type: array + items: + type: object + properties: + name: + type: string + groupId: + type: string + description: List of groups in the role + tests: + - name: "Get role users" + description: "Verify role users returns expected structure" + arguments: + - key: project_key + value: "RD" + - key: role_name + value: "Administrators" diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_roles.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_roles.yml new file mode 100644 index 0000000..b9f5a6d --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/get_project_roles.yml @@ -0,0 +1,45 @@ +mxcp: 1 + +tool: + name: get_project_roles + description: | + Get all roles available in a project. + Returns a list of roles with their IDs and URLs. + type: tool + annotations: + title: Get Project Roles + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: project_key + type: string + description: | + The project key (e.g., 'TEST' for project TEST). + This is the short identifier for the project. + examples: [ + "TEST", + "PROJ", + "DEV" + ] + return: + type: array + items: + type: object + properties: + name: + type: string + description: The role name + id: + type: string + description: The role ID + tests: + - name: "Get project roles" + description: "Verify project roles returns array of roles" + arguments: + - key: project_key + value: "RD" diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/get_user.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/get_user.yml new file mode 100644 index 0000000..80dc3fa --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/get_user.yml @@ -0,0 +1,53 @@ +mxcp: 1 + +tool: + name: get_user + description: | + Get a specific user by their unique account ID. + Returns detailed user information including display name, email, and account status. + type: tool + annotations: + title: Get User + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: account_id + type: string + description: | + The unique Atlassian account ID for the user. + This is typically in the format: "557058:ab168c94-8485-405c-88e6-6458375eb30b" + You can get account IDs from other API calls like get_issue or search_user. + examples: [ + "557058:ab168c94-8485-405c-88e6-6458375eb30b", + "5b10ac8d82e05b22cc7d4ef5", + "712020:0e99e8b3-7b3a-4b7c-9a1f-9e5d8c7b4a3e" + ] + return: + type: object + properties: + accountId: + type: string + description: The account ID + displayName: + type: string + description: The display name + emailAddress: + type: string + description: The email address + active: + type: boolean + description: Whether the user is active + timeZone: + type: string + description: The user's time zone + tests: + - name: "Get user by account ID" + description: "Just run the tool" + arguments: + - key: account_id + value: "557058:ab168c94-8485-405c-88e6-6458375eb30b" diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/jql_query.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/jql_query.yml new file mode 100644 index 0000000..f5135eb --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/jql_query.yml @@ -0,0 +1,84 @@ +mxcp: 1 + +tool: + name: jql_query + description: | + Execute a JQL (Jira Query Language) query to search for issues in your Jira instance. + Returns a list of issues with their details. + Use the start and limit parameters to paginate through large result sets. + type: tool + annotations: + title: JQL Query + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: query + type: string + description: | + The JQL query string to execute. Examples: + - "project = TEST" to find all issues in the TEST project + - "assignee = currentUser()" to find issues assigned to you + - "status = 'In Progress'" to find issues in progress + examples: [ + "project = TEST", + "status = 'In Progress'", + "project = TEST AND status = 'Done'", + "created >= -30d ORDER BY created DESC" + ] + - name: start + type: integer + description: | + The index of the first result to return (0-based). + Use this for pagination: start=0 for first page, start=50 for second page, etc. + Defaults to 0 if not specified. + default: 0 + examples: [0, 50, 100] + - name: limit + type: integer + description: | + Maximum number of results to return. + If not specified, returns all matching results. + Recommended to use with start parameter for pagination. + examples: [50, 100, 200] + default: null + return: + type: array + items: + type: object + properties: + key: + type: string + summary: + type: string + status: + type: string + assignee: + type: string + reporter: + type: string + created: + type: string + updated: + type: string + url: + type: string + tests: + - name: "Basic project query" + description: "Verify JQL query returns array of issues" + arguments: + - key: query + value: "project = RD" + - key: limit + value: 1 + - name: "Status filter query" + description: "Verify JQL query with status filter" + arguments: + - key: query + value: "status = 'In Progress'" + - key: limit + value: 1 diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/list_projects.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/list_projects.yml new file mode 100644 index 0000000..d0eab95 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/list_projects.yml @@ -0,0 +1,42 @@ +mxcp: 1 + +tool: + name: list_projects + description: | + Return a concise list of Jira projects. + Returns basic project information including key, name, type, and lead. + type: tool + annotations: + title: List Projects + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: [] + return: + type: array + items: + type: object + properties: + key: + type: string + description: The project key + name: + type: string + description: The project name + type: + type: string + description: The project type + lead: + type: string + description: The project lead + url: + type: string + description: The project URL + tests: + - name: "List all projects" + description: "Verify projects list returns array of projects" + arguments: [] diff --git a/skills/mxcp-expert/assets/project-templates/jira/tools/search_user.yml b/skills/mxcp-expert/assets/project-templates/jira/tools/search_user.yml new file mode 100644 index 0000000..551f179 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/jira/tools/search_user.yml @@ -0,0 +1,56 @@ +mxcp: 1 + +tool: + name: search_user + description: | + Search for users by query string (username, email, or display name). + Returns a list of matching users with their details. + type: tool + annotations: + title: Search User + readOnlyHint: true + destructiveHint: false + idempotentHint: true + openWorldHint: true + language: python + source: + file: ../python/jira_endpoints.py + parameters: + - name: query + type: string + description: | + Search term - can be username, email, display name, or partial matches. + The search is case-insensitive and supports partial matching. + examples: [ + "ben@raw-labs.com", + "Benjamin Gaidioz", + "ben", + "benjamin", + "gaidioz" + ] + return: + type: array + items: + type: object + properties: + accountId: + type: string + description: The account ID + displayName: + type: string + description: The display name + emailAddress: + type: string + description: The email address + active: + type: boolean + description: Whether the user is active + timeZone: + type: string + description: The user's time zone + tests: + - name: "Search by name" + description: "Verify user search by name returns results" + arguments: + - key: query + value: "Ben" diff --git a/skills/mxcp-expert/assets/project-templates/keycloak/README.md b/skills/mxcp-expert/assets/project-templates/keycloak/README.md new file mode 100644 index 0000000..d4ac7af --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/keycloak/README.md @@ -0,0 +1,76 @@ +# Keycloak Authentication Example + +This example demonstrates how to configure MXCP with Keycloak authentication. + +## Prerequisites + +1. A running Keycloak server (see quick start below) +2. MXCP installed (`pip install mxcp`) + +## Quick Start with Docker + +Run Keycloak using Docker: + +```bash +docker run -p 8080:8080 \ + -e KC_BOOTSTRAP_ADMIN_USERNAME=admin \ + -e KC_BOOTSTRAP_ADMIN_PASSWORD=admin \ + quay.io/keycloak/keycloak:latest start-dev +``` + +## Keycloak Setup + +1. Access the admin console at http://localhost:8080/admin +2. Login with username: `admin`, password: `admin` +3. Create a new realm (or use the default `master` realm) +4. Create a new client: + - Client ID: `mxcp-demo` + - Client authentication: ON + - Valid redirect URIs: `http://localhost:8000/*` +5. Copy the client secret from the Credentials tab + +## Configuration + +Set environment variables: + +```bash +export KEYCLOAK_CLIENT_ID="mxcp-demo" +export KEYCLOAK_CLIENT_SECRET="your-client-secret" +export KEYCLOAK_REALM="master" # or your custom realm +export KEYCLOAK_SERVER_URL="http://localhost:8080" +``` + +## Running the Example + +1. Start the MXCP server: + ```bash + cd examples/keycloak + mxcp serve --debug + ``` + +2. In another terminal, connect with the MCP client: + ```bash + mcp connect http://localhost:8000 + ``` + +3. You'll be redirected to Keycloak for authentication + +## Testing Tools + +Once authenticated, try running these example tools: + +```bash +# Get current user info +mcp run tool get_user_info + +# Query data with user context +mcp run resource user_data +``` + +## Production Considerations + +- Use HTTPS for all URLs in production +- Configure proper redirect URIs +- Set up appropriate Keycloak realm roles and permissions +- Enable refresh token rotation +- Configure session timeouts \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/keycloak/config.yml b/skills/mxcp-expert/assets/project-templates/keycloak/config.yml new file mode 100644 index 0000000..7b87251 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/keycloak/config.yml @@ -0,0 +1,26 @@ +mxcp: 1 +projects: + keycloak-demo: + profiles: + dev: + secrets: + - name: keycloak_creds + type: oauth + parameters: + provider: keycloak + auth: + provider: keycloak + keycloak: + client_id: "${KEYCLOAK_CLIENT_ID}" + client_secret: "${KEYCLOAK_CLIENT_SECRET}" + realm: "${KEYCLOAK_REALM}" + server_url: "${KEYCLOAK_SERVER_URL}" + scope: "openid profile email" + callback_path: "/keycloak/callback" + clients: + - client_id: "mcp-cli" + name: "MCP CLI Client" + redirect_uris: + - "http://127.0.0.1:49153/oauth/callback" + scopes: + - "mxcp:access" \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/keycloak/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/keycloak/mxcp-site.yml new file mode 100644 index 0000000..5bfd147 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/keycloak/mxcp-site.yml @@ -0,0 +1,5 @@ +mxcp: 1 +project: keycloak-demo +profile: dev +secrets: + - keycloak_creds \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/keycloak/tools/get_user_info.yml b/skills/mxcp-expert/assets/project-templates/keycloak/tools/get_user_info.yml new file mode 100644 index 0000000..96cb484 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/keycloak/tools/get_user_info.yml @@ -0,0 +1,23 @@ +mxcp: 1 +tool: + name: get_user_info + description: "Get information about the authenticated user" + parameters: [] + return: + type: object + properties: + username: + type: string + description: "Username of the authenticated user" + email: + type: string + description: "Email of the authenticated user" + provider: + type: string + description: "Authentication provider used" + source: + code: | + SELECT + get_username() as username, + get_user_email() as email, + get_user_provider() as provider \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/plugin/README.md b/skills/mxcp-expert/assets/project-templates/plugin/README.md new file mode 100644 index 0000000..b219a17 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/README.md @@ -0,0 +1,82 @@ +# MXCP Plugin Example + +This example demonstrates how to create and use a custom MXCP plugin. The plugin provides various UDFs (User Defined Functions) that can be used in your SQL queries. + +## Overview + +This plugin implements the Caesar cipher, a simple encryption technique where each letter in the plaintext is shifted by a fixed number of positions in the alphabet. + +## Project Structure + +``` +examples/plugin/ +├── plugins/ +│ └── my_plugin/ +│ └── __init__.py # Plugin implementation +├── tools/ +│ └── decipher.yml # Endpoint using the plugin +├── python/ # Directory for Python endpoints +├── sql/ # Directory for SQL implementations +├── config.yml # Example plugin configuration +├── mxcp-site.yml # Project configuration +└── README.md +``` + +## Configuration + +### 1. User Configuration + +The example includes two plugin configurations in `config.yml`: +- `rot1`: Rotates letters by 1 position (A->B, B->C, etc.) +- `rot10`: Rotates letters by 10 positions (A->K, B->L, etc.) + +To use the plugin, register these configurations in your MXCP user config (`~/.mxcp/config.yml`): + +```yaml +mxcp: 1 + +projects: + demo-plugin: + profiles: + dev: + plugin: + config: + rot1: + rotation: "1" + rot10: + rotation: "10" +``` + +Then in your `mxcp-site.yml`, you can reference one of these configurations: + +```yaml +mxcp: 1 +project: demo-plugin +profile: dev +plugin: + - name: str_secret + module: my_plugin + config: rot1 + - name: tricky + module: my_plugin + config: rot10 +``` + +## Running the MCP + +To run the service using the example configuration: + +1. Set the `MXCP_CONFIG` environment variable to point to the example's config file: + ```bash + export MXCP_CONFIG=/path/to/examples/plugin/config.yml + ``` + +2. Start the MXCP server: + ```bash + mxcp serve + ``` + +The service will now use the example configuration with both the `simple` (rot1) and `tricky` (rot10) Caesar cipher plugins. + + + diff --git a/skills/mxcp-expert/assets/project-templates/plugin/config.yml b/skills/mxcp-expert/assets/project-templates/plugin/config.yml new file mode 100644 index 0000000..767d8f1 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/config.yml @@ -0,0 +1,12 @@ +mxcp: 1 + +projects: + demo-plugin: + profiles: + dev: + plugin: + config: + rot1: + rotation: "1" # Rotate by 1 position (A->B, B->C, etc.) + rot10: + rotation: "10" # Rotate by 10 positions (A->K, B->L, etc.) diff --git a/skills/mxcp-expert/assets/project-templates/plugin/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/plugin/mxcp-site.yml new file mode 100644 index 0000000..2491235 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/mxcp-site.yml @@ -0,0 +1,7 @@ +mxcp: 1 +project: demo-plugin +profile: dev +plugin: + - name: str_secret + module: my_plugin + config: rot1 diff --git a/skills/mxcp-expert/assets/project-templates/plugin/plugins/README.md b/skills/mxcp-expert/assets/project-templates/plugin/plugins/README.md new file mode 100644 index 0000000..7f6b0d0 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/plugins/README.md @@ -0,0 +1,33 @@ +# MXCP Plugins Directory + +This directory contains MXCP plugins that extend DuckDB with custom User Defined Functions (UDFs). + +## Structure + +Each plugin should be a Python module containing a class named `MXCPPlugin` that inherits from `MXCPBasePlugin`. + +``` +plugins/ +├── my_plugin/ +│ └── __init__.py # Contains MXCPPlugin class +├── utils/ +│ └── string_utils.py +└── integrations/ + └── api_plugin.py +``` + +## Usage + +Plugins are referenced in `mxcp-site.yml`: + +```yaml +plugin: + - name: cipher + module: my_plugin + config: rot13 +``` + +The functions are then available in SQL as `{function_name}_{plugin_name}`: +```sql +SELECT encrypt_cipher('hello world'); +``` \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/plugin/plugins/my_plugin/__init__.py b/skills/mxcp-expert/assets/project-templates/plugin/plugins/my_plugin/__init__.py new file mode 100644 index 0000000..d5c4c33 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/plugins/my_plugin/__init__.py @@ -0,0 +1,134 @@ +""" +Example MXCP Plugin + +This plugin demonstrates how to create a simple MXCP plugin with Caesar cipher encryption capabilities +and optional user context integration for authentication-aware features. + +Example usage: + >>> plugin = MXCPPlugin({"rotation": 13}) + >>> plugin.encrypt("Hello, World!") # Returns "Uryyb, Jbeyq!" + >>> plugin.decrypt("Uryyb, Jbeyq!") # Returns "Hello, World!" + +With user context (when authentication is enabled): + >>> plugin.get_user_info() # Returns user information +""" + +from typing import Any, Dict, Optional + +from mxcp.plugins import MXCPBasePlugin, udf + + +class MXCPPlugin(MXCPBasePlugin): + """Plugin that provides Caesar cipher encryption and decryption functions. + + This plugin implements the Caesar cipher, a type of substitution cipher where + each letter in the plaintext is shifted a certain number of places down or up + the alphabet. It also demonstrates how to use user context for authentication-aware features. + + Example: + >>> plugin = MXCPPlugin({"rotation": 13}) + >>> plugin.encrypt("Hello, World!") # Returns "Uryyb, Jbeyq!" + >>> plugin.decrypt("Uryyb, Jbeyq!") # Returns "Hello, World!" + """ + + def __init__(self, config: Dict[str, Any], user_context=None): + """Initialize the plugin with configuration and optional user context. + + Args: + config: Configuration dictionary containing: + - rotation: Number of positions to shift (1-25), can be string or int + user_context: Optional authenticated user context (for new plugins) + """ + super().__init__(config, user_context) + rotation = config.get("rotation", 13) + + # Convert string to int if needed + if isinstance(rotation, str): + try: + rotation = int(rotation) + except ValueError: + raise ValueError("Rotation must be a valid integer") + + if not isinstance(rotation, int) or rotation < 1 or rotation > 25: + raise ValueError("Rotation must be an integer between 1 and 25") + + self.rotation = rotation + + def __rotate_char(self, char: str, forward: bool = True) -> str: + """Rotate a single character by the configured number of positions. + + Args: + char: Character to rotate + forward: True for encryption, False for decryption + + Returns: + Rotated character + """ + if not char.isalpha(): + return char + + # Determine the base ASCII value (a=97, A=65) + base = ord("a") if char.islower() else ord("A") + # Calculate the position in the alphabet (0-25) + pos = ord(char) - base + # Apply rotation (forward or backward) + shift = self.rotation if forward else -self.rotation + # Wrap around the alphabet and convert back to character + return chr(base + ((pos + shift) % 26)) + + @udf + def encrypt(self, text: str) -> str: + """Encrypt text using the Caesar cipher. + + Args: + text: Text to encrypt + + Returns: + Encrypted text + + Example: + >>> plugin.encrypt("Hello, World!") # Returns "Uryyb, Jbeyq!" + """ + return "".join(self.__rotate_char(c, True) for c in text) + + @udf + def decrypt(self, text: str) -> str: + """Decrypt text using the Caesar cipher. + + Args: + text: Text to decrypt + + Returns: + Decrypted text + + Example: + >>> plugin.decrypt("Uryyb, Jbeyq!") # Returns "Hello, World!" + """ + return "".join(self.__rotate_char(c, False) for c in text) + + @udf + def encrypt_with_user_key(self, text: str) -> str: + """Encrypt text using a user-specific rotation based on their username. + + This demonstrates how plugins can use user context to provide + personalized functionality. + + Args: + text: Text to encrypt + + Returns: + Text encrypted with user-specific key, or standard encryption if not authenticated + """ + if self.is_authenticated(): + # Use username length as additional rotation factor + username = self.get_username() or "" + user_rotation = (self.rotation + len(username)) % 26 + # Temporarily modify rotation for this operation + original_rotation = self.rotation + self.rotation = user_rotation if user_rotation > 0 else 1 + result = self.encrypt(text) + self.rotation = original_rotation # Restore original rotation + return result + else: + # Fall back to standard encryption + return self.encrypt(text) diff --git a/skills/mxcp-expert/assets/project-templates/plugin/tools/decipher.yml b/skills/mxcp-expert/assets/project-templates/plugin/tools/decipher.yml new file mode 100644 index 0000000..fa989a5 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/plugin/tools/decipher.yml @@ -0,0 +1,20 @@ +mxcp: 1 + +tool: + name: "decipher" + description: "Decrypt an encrypted message." + parameters: + - name: message + type: string + description: "Encrypted message" + return: + type: string + source: + code: SELECT decrypt_str_secret($message); + annotations: + readOnlyHint: true + tests: + - name: quick_check + arguments: + - key: message + value: "usbmbmb" diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/README.md b/skills/mxcp-expert/assets/project-templates/python-demo/README.md new file mode 100644 index 0000000..391f98d --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/README.md @@ -0,0 +1,58 @@ +# Python Endpoints Demo + +This example demonstrates how to create and use Python-based endpoints in MXCP. + +## Features Demonstrated + +### 1. Basic Python Functions +- `analyze_numbers` - Statistical analysis with various operations +- `create_sample_data` - Database operations from Python + +### 2. Async Functions +- `process_time_series` - Demonstrates async Python endpoint + +### 3. Database Access +- Using `mxcp.runtime.db` to execute SQL queries +- Parameter binding for safe SQL execution + +## Running the Examples + +In a terminal, test the endpoints: + +```bash +# Create sample data +mxcp run tool create_sample_data --param table_name=test_data --param row_count=100 + +# Analyze numbers +mxcp run tool analyze_numbers --param numbers="[1, 2, 3, 4, 5]" --param operation=mean + +# Process time series (async function) +mxcp run tool process_time_series --param table_name=test_data --param window_days=7 +``` + +Or, if you prefer, you can also start the MXCP server and use any MCP client to call the tools: +```bash +mxcp serve +``` + +## Project Structure + +``` +python-demo/ +├── mxcp-site.yml # Project configuration +├── python/ # Python modules +│ └── data_analysis.py # Python endpoint implementations +├── tools/ # Tool definitions +│ ├── analyze_numbers.yml +│ ├── create_sample_data.yml +│ └── process_time_series.yml +└── README.md +``` + +## Key Concepts + +1. **Language Declaration**: Set `language: python` in the tool definition +2. **Function Names**: The function name must match the tool name +3. **Return Types**: Functions must return data matching the declared return type +4. **Database Access**: Use `db.execute()` for SQL queries +5. **Async Support**: Both sync and async functions are supported diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/python-demo/mxcp-site.yml new file mode 100644 index 0000000..68230a0 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/mxcp-site.yml @@ -0,0 +1,3 @@ +mxcp: 1 +project: python-demo +profile: default \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/python/data_analysis.py b/skills/mxcp-expert/assets/project-templates/python-demo/python/data_analysis.py new file mode 100644 index 0000000..a920708 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/python/data_analysis.py @@ -0,0 +1,145 @@ +""" +Example Python endpoints for data analysis. +""" + +import statistics +from datetime import datetime, timedelta + +from mxcp.runtime import config, db + + +def analyze_numbers(numbers: list, operation: str = "mean") -> dict: + """ + Analyze a list of numbers with various statistical operations. + """ + if not numbers: + return {"error": "No numbers provided"} + + operations = { + "mean": statistics.mean, + "median": statistics.median, + "mode": statistics.mode, + "stdev": statistics.stdev if len(numbers) > 1 else lambda x: 0, + "sum": sum, + "min": min, + "max": max, + } + + if operation not in operations: + return {"error": f"Unknown operation: {operation}"} + + try: + result = operations[operation](numbers) + return {"operation": operation, "result": result, "count": len(numbers)} + except Exception as e: + return {"error": str(e)} + + +def create_sample_data(table_name: str, row_count: int) -> dict: + """ + Create a sample table with test data. + """ + try: + # Drop table if exists + db.execute(f"DROP TABLE IF EXISTS {table_name}") + + # Create table + db.execute( + f""" + CREATE TABLE {table_name} ( + id INTEGER PRIMARY KEY, + name VARCHAR, + value DOUBLE, + category VARCHAR, + created_at TIMESTAMP + ) + """ + ) + + # Insert sample data + for i in range(row_count): + db.execute( + f""" + INSERT INTO {table_name} (id, name, value, category, created_at) + VALUES ( + $id, + 'Item ' || $item_num, + ROUND(RANDOM() * 1000, 2), + CASE + WHEN RANDOM() < 0.33 THEN 'A' + WHEN RANDOM() < 0.66 THEN 'B' + ELSE 'C' + END, + CURRENT_TIMESTAMP - INTERVAL ($days || ' days') + ) + """, + {"id": i + 1, "item_num": i + 1, "days": i % 30}, + ) + + return {"status": "success", "table": table_name, "rows_created": row_count} + except Exception as e: + return {"status": "error", "error": str(e)} + + +def aggregate_by_category(table_name: str) -> list: + """ + Aggregate data by category from a table. + """ + try: + results = db.execute( + f""" + SELECT + category, + COUNT(*) as count, + ROUND(AVG(value), 2) as avg_value, + ROUND(SUM(value), 2) as total_value, + MIN(value) as min_value, + MAX(value) as max_value + FROM {table_name} + GROUP BY category + ORDER BY category + """ + ) + + return results + except Exception as e: + return [{"error": str(e)}] + + +async def process_time_series(table_name: str, window_days: int = 7) -> list: + """ + Async function to process time series data with rolling windows. + """ + import asyncio + + # Simulate some async processing + await asyncio.sleep(0.1) + + results = db.execute( + f""" + WITH daily_data AS ( + SELECT + DATE_TRUNC('day', created_at) as date, + category, + COUNT(*) as daily_count, + ROUND(AVG(value), 2) as daily_avg + FROM {table_name} + GROUP BY DATE_TRUNC('day', created_at), category + ) + SELECT + date, + category, + daily_count, + daily_avg, + ROUND(AVG(daily_avg) OVER ( + PARTITION BY category + ORDER BY date + ROWS BETWEEN {window_days - 1} PRECEDING AND CURRENT ROW + ), 2) as rolling_avg + FROM daily_data + ORDER BY date DESC, category + LIMIT 50 + """ + ) + + return results diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/python/primitive_arrays.py b/skills/mxcp-expert/assets/project-templates/python-demo/python/primitive_arrays.py new file mode 100644 index 0000000..60c89e1 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/python/primitive_arrays.py @@ -0,0 +1,16 @@ +"""Example showing Python endpoints can return primitive arrays.""" + + +def show_primitive_arrays() -> list: + """Return Fibonacci sequence as array of numbers.""" + return [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] + + +def get_languages() -> list: + """Return list of programming languages.""" + return ["Python", "JavaScript", "Go", "Rust", "TypeScript"] + + +def get_pi_digits() -> list: + """Return digits of pi.""" + return [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9] diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/tools/aggregate_by_category.yml b/skills/mxcp-expert/assets/project-templates/python-demo/tools/aggregate_by_category.yml new file mode 100644 index 0000000..c8c1876 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/tools/aggregate_by_category.yml @@ -0,0 +1,28 @@ +mxcp: 1 +tool: + name: aggregate_by_category + description: Aggregate data by category with statistics + language: python + source: + file: ../python/data_analysis.py + parameters: + - name: table_name + type: string + description: Name of the table to aggregate + return: + type: array + items: + type: object + properties: + category: + type: string + count: + type: integer + avg_value: + type: number + total_value: + type: number + min_value: + type: number + max_value: + type: number diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/tools/analyze_numbers.yml b/skills/mxcp-expert/assets/project-templates/python-demo/tools/analyze_numbers.yml new file mode 100644 index 0000000..d33e9c0 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/tools/analyze_numbers.yml @@ -0,0 +1,27 @@ +mxcp: 1 +tool: + name: analyze_numbers + description: Analyze a list of numbers with statistical operations + language: python + source: + file: ../python/data_analysis.py + parameters: + - name: numbers + type: array + items: + type: number + description: List of numbers to analyze + - name: operation + type: string + enum: ["mean", "median", "mode", "stdev", "sum", "min", "max"] + default: "mean" + description: Statistical operation to perform + return: + type: object + properties: + operation: + type: string + result: + type: number + count: + type: integer diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/tools/create_sample_data.yml b/skills/mxcp-expert/assets/project-templates/python-demo/tools/create_sample_data.yml new file mode 100644 index 0000000..78f0be8 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/tools/create_sample_data.yml @@ -0,0 +1,25 @@ +mxcp: 1 +tool: + name: create_sample_data + description: Create a sample table with test data + language: python + source: + file: ../python/data_analysis.py + parameters: + - name: table_name + type: string + description: Name of the table to create + - name: row_count + type: integer + description: Number of rows to generate + minimum: 1 + maximum: 10000 + return: + type: object + properties: + status: + type: string + table: + type: string + rows_created: + type: integer diff --git a/skills/mxcp-expert/assets/project-templates/python-demo/tools/process_time_series.yml b/skills/mxcp-expert/assets/project-templates/python-demo/tools/process_time_series.yml new file mode 100644 index 0000000..c6faf7d --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/python-demo/tools/process_time_series.yml @@ -0,0 +1,21 @@ +mxcp: 1 +tool: + name: process_time_series + description: Process time series data with rolling window calculations (async) + language: python + source: + file: ../python/data_analysis.py + parameters: + - name: table_name + type: string + description: Name of the table containing time series data + - name: window_days + type: integer + default: 7 + description: Size of the rolling window in days + minimum: 1 + maximum: 365 + return: + type: array + items: + type: object diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/README.md b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/README.md new file mode 100644 index 0000000..de2f210 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/README.md @@ -0,0 +1,185 @@ +# Salesforce OAuth Demo + +This example demonstrates how to create MCP tools that interact with Salesforce using the MXCP OAuth authentication system with the `simple_salesforce` library. + +## Features Demonstrated + +### 1. MXCP OAuth Authentication +- Project-wide Salesforce OAuth configuration +- Automatic token management through MXCP authentication system +- User authentication via standard OAuth 2.0 flow +- Error handling for authentication failures + +### 2. Salesforce API Integration +- `list_sobjects` - Retrieve all available Salesforce objects (sObjects) from your org with optional filtering +- `describe_sobject` - Get detailed metadata for a specific Salesforce object, including field information +- `get_sobject` - Retrieve a specific Salesforce record by its ID +- `search` - Search across all searchable Salesforce objects using simple search terms +- `soql` - Execute SOQL (Salesforce Object Query Language) queries +- `sosl` - Execute SOSL (Salesforce Object Search Language) queries for complex searches +- `whoami` - Display information about the current authenticated Salesforce user +- Token-based API access using authenticated user context + +## Prerequisites + +1. **Salesforce Org**: You need access to a Salesforce org (Developer Edition is fine) +2. **Salesforce Connected App**: Create a Connected App in Salesforce with OAuth settings +3. **Python Dependencies**: The `simple_salesforce` library (automatically managed by MXCP) + +## Setup + +### 1. Create Salesforce Connected App + +1. Log into your Salesforce org +2. Go to **Setup** → **App Manager** → **New Connected App** +3. Fill in basic information: + - **Connected App Name**: "MXCP Integration" (or your preferred name) + - **API Name**: Will auto-populate + - **Contact Email**: Your email +4. Enable OAuth Settings: + - **Enable OAuth Settings**: Check this box + - **Callback URL**: This depends on your deployment: + - **Local Development**: `http://localhost:8000/salesforce/callback` + - **Remote/Production**: `https://your-domain.com/salesforce/callback` (replace with your actual server URL) + - **Selected OAuth Scopes**: Add these scopes: + - Access and manage your data (api) + - Perform requests on your behalf at any time (refresh_token, offline_access) + - Access your basic information (id, profile, email, address, phone) +5. Save the Connected App +6. Note down the **Consumer Key** (Client ID) and **Consumer Secret** (Client Secret) + +### 2. Configure Environment Variables + +Set your Salesforce OAuth credentials: +```bash +export SALESFORCE_CLIENT_ID="your-consumer-key-from-connected-app" +export SALESFORCE_CLIENT_SECRET="your-consumer-secret-from-connected-app" +``` + +### 3. Configure Callback URL for Your Deployment + +The callback URL configuration depends on where your MXCP server will run: + +#### Local Development +For local development, the default configuration in `config.yml` uses `http://localhost:8000/salesforce/callback`. This works when: +- You're running MXCP locally on your development machine +- Users authenticate from the same machine where MXCP is running + +#### Remote/Production Deployment +For remote servers or production deployments, you need to: + +1. **Update config.yml**: Uncomment and modify the production callback URL: + ```yaml + redirect_uris: + - "http://localhost:8000/salesforce/callback" # Keep for local dev + - "https://your-domain.com/salesforce/callback" # Add your actual URL + ``` + +2. **Update base_url**: Set the correct base URL in your config: + ```yaml + transport: + http: + base_url: https://your-domain.com # Your actual server URL + ``` + +3. **Configure Connected App**: Add the production callback URL to your Salesforce Connected App's callback URLs + +**Important**: +- The callback URL must be accessible from the user's browser, not just from your server +- For production deployments, Salesforce requires HTTPS for callback URLs +- You can configure multiple callback URLs in your Connected App to support both local development and production + +## Authenticate with Salesforce + +When you first run MXCP, you'll need to authenticate with Salesforce: + +```bash +# Start the MXCP server with the config file - this will prompt for authentication +MXCP_CONFIG=config.yml mxcp serve +``` + +The authentication flow will: +1. Open your browser to Salesforce login +2. You'll log in with your Salesforce credentials +3. Authorize the MXCP application +4. Redirect back to complete authentication + + +## Project Structure + +``` +salesforce-oauth/ +├── mxcp-site.yml # Project metadata +├── config.yml # Server and authentication configuration +├── python/ # Python modules +│ └── salesforce_client.py # Salesforce API implementations +├── tools/ # Tool definitions +│ ├── list_sobjects.yml # List all Salesforce objects +│ ├── describe_sobject.yml # Get object metadata +│ ├── get_sobject.yml # Get record by ID +│ ├── search.yml # Search across objects +│ ├── soql.yml # Execute SOQL queries +│ ├── sosl.yml # Execute SOSL queries +│ └── whoami.yml # Current user information +└── README.md # This file +``` + +## Key Concepts + +1. **MXCP OAuth Integration**: Uses MXCP's built-in Salesforce OAuth provider for secure authentication +2. **User Context**: Access tokens are automatically managed and provided through `user_context()` +3. **Token-based Authentication**: simple_salesforce is initialized with OAuth tokens instead of credentials +4. **Project-wide Configuration**: Authentication is configured at the project level in `mxcp-site.yml` +5. **Error Handling**: Comprehensive error handling for authentication and API failures +6. **API Integration**: Demonstrates calling Salesforce REST API endpoints with proper OAuth tokens + +## Example Output + +When you run `list_sobjects`, you'll get a response like: + +```json +[ + "Account", + "Contact", + "Lead", + "Opportunity", + "Case", + "Product2", + "Task", + "Event", + "User", + "CustomObject__c", + ... +] +``` + +## Troubleshooting + +### Authentication Errors +- **"No user context available"**: User needs to authenticate first by running `mxcp serve` and completing OAuth flow +- **"No Salesforce access token found"**: Authentication was incomplete or token expired - re-authenticate +- **Connected App Issues**: Verify your `SALESFORCE_CLIENT_ID` and `SALESFORCE_CLIENT_SECRET` are correct +- **Callback URL Mismatch**: Ensure the callback URL in your Connected App matches where your MXCP server is accessible: + - Local development: `http://localhost:8000/salesforce/callback` + - Remote/production: `https://your-domain.com/salesforce/callback` +- **OAuth Scopes**: Verify your Connected App has the required OAuth scopes (api, refresh_token, id, profile, email) + +### API Errors +- Verify you have the necessary permissions in Salesforce +- Check that your org is accessible and not in maintenance mode +- Ensure your Connected App is approved and not restricted by IP ranges + +### Connected App Setup Issues +- **App Not Found**: Make sure your Connected App is saved and the Consumer Key/Secret are copied correctly +- **Callback URL**: The callback URL must exactly match your MXCP server's accessible address: + - For local development: `http://localhost:8000/salesforce/callback` + - For remote deployment: `https://your-domain.com/salesforce/callback` +- **OAuth Scopes**: Missing scopes will cause authentication to fail - ensure all required scopes are selected + +## Next Steps + +This example demonstrates a comprehensive set of Salesforce integration tools. You could extend it with additional tools for data manipulation like: +- `create_record` - Create new records in Salesforce objects +- `update_record` - Update existing records +- `delete_record` - Delete records +- `bulk_operations` - Handle bulk data operations for large datasets \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/config.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/config.yml new file mode 100644 index 0000000..7d2b684 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/config.yml @@ -0,0 +1,22 @@ +mxcp: 1 +transport: + http: + port: 8000 + host: 0.0.0.0 + # Set base_url to your server's public URL for production + base_url: http://localhost:8000 + +projects: + salesforce-oauth: + profiles: + default: + # OAuth Authentication Configuration + auth: + provider: salesforce + salesforce: + client_id: "${SALESFORCE_CLIENT_ID}" + client_secret: "${SALESFORCE_CLIENT_SECRET}" + scope: "api refresh_token openid profile email" + callback_path: "/salesforce/callback" + auth_url: "https://login.salesforce.com/services/oauth2/authorize" + token_url: "https://login.salesforce.com/services/oauth2/token" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/mxcp-site.yml new file mode 100644 index 0000000..9e91062 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/mxcp-site.yml @@ -0,0 +1,3 @@ +mxcp: 1 +project: salesforce-oauth +profile: default \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/python/salesforce_client.py b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/python/salesforce_client.py new file mode 100644 index 0000000..cde9350 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/python/salesforce_client.py @@ -0,0 +1,466 @@ +""" +Salesforce MCP tools using simple_salesforce with MXCP OAuth authentication. +""" + +import threading +from functools import wraps +from typing import Dict, Any, List, Optional +from mxcp.sdk.auth.context import get_user_context +from simple_salesforce import Salesforce # type: ignore[attr-defined] +from simple_salesforce.exceptions import SalesforceExpiredSession +from mxcp.runtime import on_init, on_shutdown +import logging + +# Thread-safe cache for Salesforce clients +_client_cache: Optional[Dict[str, Salesforce]] = None +_cache_lock: Optional[threading.Lock] = None + + +@on_init +def init_client_cache() -> None: + """ + Initialize the Salesforce client cache. + """ + global _client_cache, _cache_lock + _client_cache = {} + _cache_lock = threading.Lock() + + +@on_shutdown +def clear_client_cache() -> None: + """ + Clear the Salesforce client cache. + """ + global _client_cache, _cache_lock + _client_cache = None + _cache_lock = None + + +def _get_cache_key(context: Any) -> Optional[str]: + """Generate a cache key based on user context.""" + if not context: + return None + + # Use user ID and instance URL as cache key + user_id = getattr(context, "user_id", None) or getattr(context, "id", None) + + # Extract instance URL + instance_url = None + if context.raw_profile and "urls" in context.raw_profile: + urls = context.raw_profile["urls"] + instance_url = urls.get("custom_domain") + if not instance_url: + for url_key in ["rest", "enterprise", "partner"]: + if url_key in urls: + service_url = urls[url_key] + instance_url = service_url.split("/services/")[0] + break + + if user_id and instance_url: + return f"{user_id}:{instance_url}" + + return None + + +def with_session_retry(func: Any) -> Any: + """ + Decorator that automatically retries API calls with cache invalidation when sessions expire. + + This handles the race condition where a session might expire between cache validation + and the actual API call. + """ + + @wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + try: + return func(*args, **kwargs) + except SalesforceExpiredSession: + logging.error("Salesforce session expired") + # Session expired during the call - invalidate cache and retry once + context = get_user_context() + cache_key = _get_cache_key(context) + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + # Remove the expired client from cache + _client_cache.pop(cache_key, None) + + # Retry the function call - this will get a fresh client + return func(*args, **kwargs) + + return wrapper + + +def _escape_sosl_search_term(search_term: str) -> str: + """ + Escape special characters in SOSL search terms to prevent injection attacks. + + SOSL special characters that need escaping: & | ! { } [ ] ( ) ^ ~ * ? : " ' + - + """ + # Escape backslashes first to avoid double-escaping + escaped = search_term.replace("\\", "\\\\") + + # Escape SOSL special characters + special_chars = [ + "&", + "|", + "!", + "{", + "}", + "[", + "]", + "(", + ")", + "^", + "~", + "*", + "?", + ":", + '"', + "'", + "+", + "-", + ] + for char in special_chars: + escaped = escaped.replace(char, f"\\{char}") + + return escaped + + +def _get_salesforce_client() -> Salesforce: + """ + Create and return an authenticated Salesforce client using OAuth tokens from user_context. + + Uses caching to avoid recreating clients unnecessarily. Clients are cached per user + and instance URL combination in a thread-safe manner. + """ + try: + # Get the authenticated user's context + context = get_user_context() + + if not context: + raise ValueError("No user context available. User must be authenticated.") + + # Generate cache key + cache_key = _get_cache_key(context) + + # Try to get cached client first + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + if cache_key in _client_cache: + logging.info("Using cached Salesforce client") + # Return cached client - retry decorator will handle any session expiry + return _client_cache[cache_key] + + logging.info("No cached Salesforce client found, creating new one") + # Extract Salesforce OAuth tokens from user context + access_token = context.external_token + + # Extract instance URL from user context (this is user/org-specific) + instance_url = None + if context.raw_profile and "urls" in context.raw_profile: + urls = context.raw_profile["urls"] + # Try custom_domain first (this is the full instance URL) + instance_url = urls.get("custom_domain") + if not instance_url: + # Fallback: extract base URL from any service endpoint + for url_key in ["rest", "enterprise", "partner"]: + if url_key in urls: + service_url = urls[url_key] + instance_url = service_url.split("/services/")[0] + break + + if not access_token: + raise ValueError( + "No Salesforce access token found in user context. " + "User must authenticate with Salesforce through MXCP." + ) + + if not instance_url: + raise ValueError( + "No Salesforce instance URL found in user context. " + "Authentication may be incomplete or profile missing URL information." + ) + + # Initialize Salesforce client with OAuth token + sf = Salesforce(session_id=access_token, instance_url=instance_url) + + # Cache the client if we have a valid cache key + if cache_key and _cache_lock and _client_cache: + with _cache_lock: + _client_cache[cache_key] = sf + + return sf + + except Exception as e: + raise ValueError(f"Failed to authenticate with Salesforce: {str(e)}") + + +@with_session_retry +def list_sobjects(filter: Optional[str] = None) -> List[str]: + """ + List all available Salesforce objects (sObjects) in the org. + + Args: + filter: Optional fuzzy filter to match object names (case-insensitive substring search). + Examples: "account", "__c" for custom objects, "contact", etc. + + Returns: + list: List of Salesforce object names as strings + """ + try: + sf = _get_salesforce_client() + + # Get all sObjects metadata + describe_result = sf.describe() + + if not describe_result or "sobjects" not in describe_result: + raise ValueError("Invalid describe response from Salesforce API") + + # Extract just the object names + sobjects = describe_result["sobjects"] + object_names = [] + for obj in sobjects: + if not isinstance(obj, dict) or "name" not in obj: + raise ValueError(f"Invalid sobject format: {obj}") + object_names.append(obj["name"]) + + # Apply fuzzy filter if provided + if filter is not None and filter.strip(): + filter_lower = filter.lower() + object_names = [name for name in object_names if filter_lower in name.lower()] + + # Sort alphabetically for consistent output + object_names.sort() + + return object_names + + except Exception as e: + # Return error in a format that can be handled by the caller + raise Exception(f"Error listing Salesforce objects: {str(e)}") + + +@with_session_retry +def describe_sobject(object_name: str) -> Dict[str, Any]: + """ + Get detailed field information for a specific Salesforce object (sObject). + + Args: + object_name: The API name of the Salesforce object to describe + + Returns: + dict: Dictionary where each key is a field name and each value contains field metadata + """ + sf = _get_salesforce_client() + + # Try to get the object - catch this specifically for "object doesn't exist" + try: + sobject = getattr(sf, object_name) + except AttributeError: + raise Exception(f"Salesforce object '{object_name}' does not exist") + + # Let API errors from describe() propagate naturally with their original messages + describe_result = sobject.describe() + + if not describe_result or "fields" not in describe_result: + raise ValueError(f"Invalid describe response for object '{object_name}'") + + # Process fields into the required format + fields_info = {} + for field in describe_result["fields"]: + if not isinstance(field, dict): + raise ValueError(f"Invalid field format in '{object_name}': {field}") + + required_fields = ["name", "type", "label"] + for required_field in required_fields: + if required_field not in field: + raise ValueError(f"Field missing '{required_field}' in '{object_name}': {field}") + field_name = field["name"] + field_info = {"type": field["type"], "label": field["label"]} + + # Add referenceTo information for reference fields + if field["type"] == "reference" and field.get("referenceTo"): + field_info["referenceTo"] = field["referenceTo"] + + fields_info[field_name] = field_info + + return fields_info + + +@with_session_retry +def get_sobject(object_name: str, record_id: str) -> Dict[str, Any]: + """ + Retrieve a specific Salesforce record by its object type and ID. + + Args: + object_name: The API name of the Salesforce object type + record_id: The unique Salesforce ID of the record to retrieve + + Returns: + dict: Dictionary containing all fields and values for the specified record + """ + sf = _get_salesforce_client() + + # Try to get the object - catch this specifically for "object doesn't exist" + try: + sobject = getattr(sf, object_name) + except AttributeError: + raise Exception(f"Salesforce object '{object_name}' does not exist") + + # Let API errors from get() propagate naturally with their original messages + record = sobject.get(record_id) + + if not isinstance(record, dict): + raise ValueError(f"Invalid record format returned for {object_name}:{record_id}") + + # Remove 'attributes' field for consistency with other functions + clean_record: Dict[str, Any] = {k: v for k, v in record.items() if k != "attributes"} + return clean_record + + +@with_session_retry +def soql(query: str) -> List[Dict[str, Any]]: + """ + Execute an arbitrary SOQL (Salesforce Object Query Language) query. + + Args: + query: The SOQL query to execute + + Returns: + list: Array of records returned by the SOQL query + """ + sf = _get_salesforce_client() + + # Execute the SOQL query + result = sf.query(query) + + if not result or "records" not in result: + raise ValueError("Invalid SOQL query response from Salesforce API") + + # Remove 'attributes' field from each record for cleaner output + records = [] + for record in result["records"]: + if not isinstance(record, dict): + raise ValueError(f"Invalid record format in SOQL result: {record}") + clean_record = {k: v for k, v in record.items() if k != "attributes"} + records.append(clean_record) + + return records + + +@with_session_retry +def search(search_term: str) -> List[Dict[str, Any]]: + """ + Search for records across all searchable Salesforce objects using a simple search term. + Uses Salesforce's native search to automatically find matches across all objects. + + Args: + search_term: The term to search for across Salesforce objects + + Returns: + list: Array of matching records from various Salesforce objects + """ + sf = _get_salesforce_client() + + # Escape the search term to prevent SOSL injection attacks + escaped_search_term = _escape_sosl_search_term(search_term) + + # Use simple SOSL syntax - Salesforce searches all searchable objects automatically + sosl_query = f"FIND {{{escaped_search_term}}}" + + # Execute the SOSL search + search_results = sf.search(sosl_query) + + if not search_results or "searchRecords" not in search_results: + raise ValueError("Invalid SOSL search response from Salesforce API") + + # Flatten results from all objects into a single array + all_records = [] + for record in search_results["searchRecords"]: + if not isinstance(record, dict): + raise ValueError(f"Invalid record format in SOSL result: {record}") + if "attributes" not in record or not isinstance(record["attributes"], dict): + raise ValueError(f"Invalid record attributes in SOSL result: {record}") + + # Remove 'attributes' field and add object type for context + clean_record = {k: v for k, v in record.items() if k != "attributes"} + clean_record["_ObjectType"] = record["attributes"]["type"] + all_records.append(clean_record) + + return all_records + + +@with_session_retry +def sosl(query: str) -> List[Dict[str, Any]]: + """ + Execute an arbitrary SOSL (Salesforce Object Search Language) query. + + Args: + query: The SOSL query to execute + + Returns: + list: Array of records returned by the SOSL search query + """ + sf = _get_salesforce_client() + + # Execute the SOSL search + search_results = sf.search(query) + + if not search_results or "searchRecords" not in search_results: + raise ValueError("Invalid SOSL query response from Salesforce API") + + # Flatten results from all objects into a single array + all_records = [] + for record in search_results["searchRecords"]: + if not isinstance(record, dict): + raise ValueError(f"Invalid record format in SOSL result: {record}") + if "attributes" not in record or not isinstance(record["attributes"], dict): + raise ValueError(f"Invalid record attributes in SOSL result: {record}") + + # Remove 'attributes' field and add object type for context + clean_record = {k: v for k, v in record.items() if k != "attributes"} + clean_record["_ObjectType"] = record["attributes"]["type"] + all_records.append(clean_record) + + return all_records + + +def whoami() -> Dict[str, Any]: + """ + Get basic information about the currently authenticated Salesforce user from the user context. + + Returns essential user information from the MXCP authentication context without making API calls. + + Returns: + dict: Dictionary containing essential current user information + """ + context = get_user_context() + + if not context: + raise ValueError("No user context available. User must be authenticated.") + + # Extract instance URL from context + instance_url = None + if context.raw_profile and "urls" in context.raw_profile: + urls = context.raw_profile["urls"] + instance_url = urls.get("custom_domain") + if not instance_url: + # Fallback: extract base URL from any service endpoint + for url_key in ["rest", "enterprise", "partner"]: + if url_key in urls: + service_url = urls[url_key] + instance_url = service_url.split("/services/")[0] + break + + # Extract essential user information from raw profile + raw_profile = context.raw_profile or {} + + user_info = { + "user_id": raw_profile.get("user_id"), + "email": raw_profile.get("email"), + "name": raw_profile.get("name"), + "preferred_username": raw_profile.get("preferred_username"), + "organization_id": raw_profile.get("organization_id"), + "instanceUrl": instance_url, + } + + return user_info diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/describe_sobject.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/describe_sobject.yml new file mode 100644 index 0000000..35a3cbe --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/describe_sobject.yml @@ -0,0 +1,34 @@ +mxcp: 1 +tool: + name: describe_sobject + description: | + Get detailed metadata for a specific Salesforce object, including all field information. + Returns field names, types, labels, and relationship details. + tags: ["salesforce", "metadata", "schema"] + annotations: + title: "Describe Salesforce Object" + readOnlyHint: true + idempotentHint: true + parameters: + - name: object_name + type: string + description: "Name of the Salesforce object to describe" + examples: ["Account", "Contact", "Opportunity", "Lead", "Case"] + return: + type: object + description: "Object metadata with field information" + additionalProperties: true + language: python + source: + file: ../python/salesforce_client.py + tests: + - name: "Describe Account object" + description: "Test describing the standard Account object" + arguments: + - key: object_name + value: "Account" + result_contains: + Name: + type: "string" + Id: + type: "id" \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/get_sobject.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/get_sobject.yml new file mode 100644 index 0000000..28d3647 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/get_sobject.yml @@ -0,0 +1,37 @@ +mxcp: 1 +tool: + name: get_sobject + description: | + Retrieve a specific Salesforce record by its ID. + Returns the complete record data with all accessible fields. + tags: ["salesforce", "data", "records"] + annotations: + title: "Get Salesforce Record" + readOnlyHint: true + idempotentHint: true + parameters: + - name: object_name + type: string + description: "Name of the Salesforce object type" + examples: ["Account", "Contact", "Opportunity", "Lead", "Case"] + - name: record_id + type: string + description: "Salesforce record ID (15 or 18 character ID)" + examples: ["001000000000001", "003000000000001AAA"] + return: + type: object + description: "Complete record data" + additionalProperties: true + language: python + source: + file: ../python/salesforce_client.py + tests: + - name: "Get Account record" + description: "Test retrieving an Account record by ID" + arguments: + - key: object_name + value: "Account" + - key: record_id + value: "001000000000001" + result_contains: + Id: "001000000000001" \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/list_sobjects.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/list_sobjects.yml new file mode 100644 index 0000000..babf235 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/list_sobjects.yml @@ -0,0 +1,38 @@ +mxcp: 1 +tool: + name: list_sobjects + description: | + List all available Salesforce objects (sObjects) in the organization. + Optionally filter the list by providing a filter term for fuzzy matching on object names. + tags: ["salesforce", "metadata", "objects"] + annotations: + title: "List Salesforce Objects" + readOnlyHint: true + idempotentHint: true + parameters: + - name: filter + type: string + description: "Optional filter term to match against object names (case-insensitive fuzzy matching)" + default: null + examples: ["Account", "Contact", "Custom"] + return: + type: array + description: "List of sObject names" + items: + type: string + description: "Name of a Salesforce object" + language: python + source: + file: ../python/salesforce_client.py + tests: + - name: "List all objects" + description: "Test listing all available Salesforce objects" + arguments: [] + result_contains_item: "Account" + + - name: "Filter objects" + description: "Test filtering objects by name" + arguments: + - key: filter + value: "Account" + result_contains_item: "Account" \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/search.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/search.yml new file mode 100644 index 0000000..70c0347 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/search.yml @@ -0,0 +1,38 @@ +mxcp: 1 +tool: + name: search + description: | + Search across all searchable Salesforce objects using the native Salesforce search. + This uses the simple SOSL syntax "FIND {search_term}" which automatically searches + all searchable objects and fields. + language: python + source: + file: ../python/salesforce_client.py + parameters: + - name: search_term + type: string + description: "Term to search for across all searchable objects" + examples: ["John", "Acme", "555-1234", "example.com"] + return: + type: array + description: "Search results from all matching objects" + items: + type: object + description: "Search result record" + additionalProperties: true + tags: + - salesforce + - search + - data + annotations: + title: "Search Salesforce Records" + readOnlyHint: true + idempotentHint: true + tests: + - name: "Basic search" + description: "Test searching for a common term" + arguments: + - key: search_term + value: "test" + # Note: Using result type array since search results can be empty or contain records + result: [] \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/soql.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/soql.yml new file mode 100644 index 0000000..add147f --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/soql.yml @@ -0,0 +1,33 @@ +mxcp: 1 +tool: + name: soql + description: | + Execute a SOQL (Salesforce Object Query Language) query. Returns query results as an array of records. + For personalized queries (e.g., 'my tasks', 'my opportunities'), use the whoami tool first to get the current user's ID for filtering (e.g., WHERE OwnerId = 'user_id'). + tags: ["salesforce", "query", "data"] + annotations: + title: "Execute SOQL Query" + readOnlyHint: true + idempotentHint: true + parameters: + - name: query + type: string + description: "SOQL query string to execute" + examples: ["SELECT Id, Name FROM Account LIMIT 10", "SELECT Id, Email FROM Contact WHERE LastName = 'Smith'"] + return: + type: array + description: "Query results" + items: + type: object + description: "Record data" + additionalProperties: true + language: python + source: + file: ../python/salesforce_client.py + tests: + - name: "Simple Account query" + description: "Test executing a basic SOQL query on Account object" + arguments: + - key: query + value: "SELECT Id, Name FROM Account LIMIT 1" + result_length: 1 \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/sosl.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/sosl.yml new file mode 100644 index 0000000..d9113d1 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/sosl.yml @@ -0,0 +1,36 @@ +mxcp: 1 +tool: + name: sosl + description: | + Execute a raw SOSL (Salesforce Object Search Language) query. + Allows complex search queries with specific object targeting and field selection. + language: python + source: + file: ../python/salesforce_client.py + parameters: + - name: query + type: string + description: "SOSL query string to execute" + examples: ["FIND {test} IN ALL FIELDS RETURNING Account(Id, Name)", "FIND {John} RETURNING Contact(Id, Name, Email)"] + return: + type: array + description: "Search results" + items: + type: object + description: "Search result record" + additionalProperties: true + tags: + - salesforce + - search + - advanced + annotations: + title: "Execute SOSL Query" + readOnlyHint: true + idempotentHint: true + tests: + - name: "Simple SOSL query" + description: "Test executing a basic SOSL search query" + arguments: + - key: query + value: "FIND {test} IN ALL FIELDS RETURNING Account(Id, Name)" + result: [] \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/whoami.yml b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/whoami.yml new file mode 100644 index 0000000..9a45c64 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce-oauth/tools/whoami.yml @@ -0,0 +1,44 @@ +mxcp: 1 +tool: + name: whoami + title: Current User Information + description: | + Get the current authenticated user's information (user_id, email, name) from OAuth context. + Use this tool before executing personalized SOQL queries to identify the user for filtering records by ownership or assignment. + tags: + - salesforce + - user + - auth + annotations: + readOnlyHint: true + idempotentHint: true + parameters: [] + return: + type: object + description: Essential current user information from OAuth context + properties: + user_id: + type: string + description: Salesforce user ID + email: + type: string + description: User's email address + name: + type: string + description: User's full name + preferred_username: + type: string + description: User's preferred username + organization_id: + type: string + description: Salesforce organization ID + instanceUrl: + type: string + description: Salesforce instance URL for the authenticated user + language: python + source: + file: ../python/salesforce_client.py + tests: + - name: whoami_basic + description: Get current user information + arguments: [] diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/README.md b/skills/mxcp-expert/assets/project-templates/salesforce/README.md new file mode 100644 index 0000000..dbabbc7 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/README.md @@ -0,0 +1,112 @@ +# MXCP Salesforce Python Endpoints Example + +This example demonstrates how to use MXCP with Salesforce data using **Python endpoints**. + +## Overview + +This example provides Python MCP endpoints that allow you to: +- Execute SOQL queries to retrieve Salesforce data +- Execute SOSL searches across multiple objects +- List all available Salesforce objects +- Get detailed object descriptions +- Retrieve specific records by ID +- Perform simple text searches across common objects + +## Configuration + +### 1. Getting Salesforce Credentials + +To use this example, you'll need: + +1. **Salesforce Username**: Your Salesforce username (email address) +2. **Salesforce Password**: Your Salesforce password +3. **Security Token**: Your Salesforce security token (get from Setup → My Personal Information → Reset My Security Token) +4. **Instance URL**: Your Salesforce instance URL (e.g., https://your-domain.salesforce.com) +5. **Client ID**: A connected app client ID (you can use any valid client ID) + +### 2. User Configuration + +Add the following to your MXCP user config (`~/.mxcp/config.yml`): + +```yaml +mxcp: 1 + +projects: + salesforce-demo: + profiles: + dev: + secrets: + salesforce: + instance_url: "https://your-instance.salesforce.com" + username: "your-username@example.com" + password: "your-password" + security_token: "your-security-token" + client_id: "your-client-id" +``` + +### 3. Site Configuration + +Create an `mxcp-site.yml` file: + +```yaml +mxcp: 1 +project: salesforce-demo +profile: dev +secrets: + - salesforce +``` + +## Available Tools + +### SOQL Query +Execute SOQL queries directly as Python function calls: +```bash +mxcp run tool soql --param query="SELECT Id, Name FROM Account LIMIT 10" +``` + +### SOSL Search +Execute SOSL searches across multiple objects: +```bash +mxcp run tool sosl --param query="FIND {Acme} IN ALL FIELDS RETURNING Account(Name, Phone)" +``` + +### Simple Search +Perform simple text searches across common objects: +```bash +mxcp run tool search --param search_term="Acme" +``` + +### List Objects +List all available Salesforce objects: +```bash +mxcp run tool list_sobjects +``` + +### Describe Object +Get detailed information about a specific object: +```bash +mxcp run tool describe_sobject --param sobject_name="Account" +``` + +### Get Object +Get a specific record by its ID: +```bash +mxcp run tool get_sobject --param sobject_name="Account" --param record_id="001xx000003DIloAAG" +``` + +## Project Structure + +``` +salesforce/ +├── mxcp-site.yml # Site configuration +├── python/ # Python implementations +│ └── salesforce_endpoints.py # All Salesforce endpoint functions +├── tools/ # Tool definitions +│ ├── soql.yml +│ ├── sosl.yml +│ ├── search.yml +│ ├── list_sobjects.yml +│ ├── describe_sobject.yml +│ └── get_sobject.yml +└── README.md +``` \ No newline at end of file diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/config.yml b/skills/mxcp-expert/assets/project-templates/salesforce/config.yml new file mode 100644 index 0000000..ca02a83 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/config.yml @@ -0,0 +1,15 @@ +mxcp: 1 + +projects: + salesforce-demo: + profiles: + dev: + secrets: + - name: salesforce + type: python + parameters: + instance_url: "${SALESFORCE_URL}" + username: "${SALESFORCE_USERNAME}" + password: "${SALESFORCE_PASSWORD}" + security_token: "${SALESFORCE_TOKEN}" + client_id: "${SALESFORCE_CLIENT_ID}" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/mxcp-site.yml b/skills/mxcp-expert/assets/project-templates/salesforce/mxcp-site.yml new file mode 100644 index 0000000..da362c7 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/mxcp-site.yml @@ -0,0 +1,5 @@ +mxcp: 1 +project: salesforce-demo +profile: dev +secrets: + - salesforce diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/python/salesforce_endpoints.py b/skills/mxcp-expert/assets/project-templates/salesforce/python/salesforce_endpoints.py new file mode 100644 index 0000000..e911c28 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/python/salesforce_endpoints.py @@ -0,0 +1,330 @@ +""" +Salesforce Python Endpoints + +This module provides direct Python MCP endpoints for querying Salesforce. +This is a simpler alternative to the plugin-based approach. +""" + +from typing import Dict, Any, List, Optional, Callable +import logging +import time +import functools +import threading +from simple_salesforce import Salesforce # type: ignore[attr-defined] +from simple_salesforce.exceptions import SalesforceExpiredSession + +from mxcp.runtime import config, on_init, on_shutdown + +logger = logging.getLogger(__name__) + +# Global Salesforce client for reuse across all function calls +sf_client: Optional[Salesforce] = None +# Thread lock to protect client initialization +_client_lock = threading.Lock() + + +@on_init +def setup_salesforce_client() -> None: + """Initialize Salesforce client when server starts. + + Thread-safe: multiple threads can safely call this simultaneously. + """ + global sf_client + + with _client_lock: + logger.info("Initializing Salesforce client...") + + sf_config = config.get_secret("salesforce") + if not sf_config: + raise ValueError( + "Salesforce configuration not found. Please configure Salesforce secrets in your user config." + ) + + required_keys = ["username", "password", "security_token", "instance_url", "client_id"] + missing_keys = [key for key in required_keys if not sf_config.get(key)] + if missing_keys: + raise ValueError(f"Missing Salesforce configuration keys: {', '.join(missing_keys)}") + + sf_client = Salesforce( + username=sf_config["username"], + password=sf_config["password"], + security_token=sf_config["security_token"], + instance_url=sf_config["instance_url"], + client_id=sf_config["client_id"], + ) + + logger.info("Salesforce client initialized successfully") + + +@on_shutdown +def cleanup_salesforce_client() -> None: + """Clean up Salesforce client when server stops.""" + global sf_client + if sf_client: + # Salesforce client doesn't need explicit cleanup, but we'll clear the reference + sf_client = None + logger.info("Salesforce client cleaned up") + + +def retry_on_session_expiration(func: Callable[..., Any]) -> Callable[..., Any]: + """ + Decorator that automatically retries functions on session expiration. + + This only retries on SalesforceExpiredSession, not SalesforceAuthenticationFailed. + Authentication failures (wrong credentials) should not be retried. + + Retries up to 2 times on session expiration (3 total attempts). + Thread-safe: setup_salesforce_client() handles concurrent access internally. + + Usage: + @retry_on_session_expiration + def my_salesforce_function(): + # Function that might fail due to session expiration + pass + """ + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + max_retries = 2 # Hardcoded: 2 retries = 3 total attempts + + for attempt in range(max_retries + 1): + try: + return func(*args, **kwargs) + except SalesforceExpiredSession as e: + if attempt < max_retries: + logger.warning( + f"Session expired on attempt {attempt + 1} in {func.__name__}: {e}" + ) + logger.info( + f"Retrying after re-initializing client (attempt {attempt + 2}/{max_retries + 1})" + ) + + try: + setup_salesforce_client() # Thread-safe internally + time.sleep(0.1) # Small delay to avoid immediate retry + except Exception as setup_error: + logger.error(f"Failed to re-initialize Salesforce client: {setup_error}") + raise setup_error # Raise the setup error, not the original session error + else: + # Last attempt failed, re-raise the session expiration error + raise e + + return wrapper + + +def _get_salesforce_client() -> Salesforce: + """Get the global Salesforce client.""" + if sf_client is None: + raise RuntimeError( + "Salesforce client not initialized. Make sure the server is started properly." + ) + return sf_client + + +@retry_on_session_expiration +def soql(query: str) -> List[Dict[str, Any]]: + """Execute an SOQL query against Salesforce. + + Args: + query: The SOQL query to execute + + Returns: + List of records returned by the query, with 'attributes' field removed + + Example: + >>> soql("SELECT Id, Name FROM Account") + """ + logger.info("Executing SOQL query: %s", query) + + sf = _get_salesforce_client() + result = sf.query(query) + + # Remove 'attributes' field from each record for cleaner output + if "records" not in result: + raise ValueError(f"Unexpected SOQL response format: missing 'records' field in {result}") + + records = result["records"] + return [{k: v for k, v in record.items() if k != "attributes"} for record in records] + + +@retry_on_session_expiration +def sosl(query: str) -> List[Dict[str, Any]]: + """Execute a SOSL query against Salesforce. + + Args: + query: The SOSL query to execute + + Returns: + List of search results from searchRecords + + Example: + >>> sosl("FIND {Acme} IN ALL FIELDS RETURNING Account(Name), Contact(FirstName,LastName)") + """ + logger.info("Executing SOSL query: %s", query) + + sf = _get_salesforce_client() + result = sf.search(query) + + # Return the searchRecords directly as a list + if "searchRecords" not in result: + raise ValueError( + f"Unexpected SOSL response format: missing 'searchRecords' field in {result}" + ) + + search_records: List[Dict[str, Any]] = result["searchRecords"] + return search_records + + +@retry_on_session_expiration +def search(search_term: str) -> List[Dict[str, Any]]: + """Search across all Salesforce objects using a simple search term. + + Args: + search_term: The term to search for + + Returns: + List of search results + + Example: + >>> search("Acme") # Searches for "Acme" across all objects + """ + logger.info("Searching for term: %s", search_term) + + # Build a SOSL query that searches across common objects + sosl_query = f"FIND {{{search_term}}} IN ALL FIELDS RETURNING Account(Name, Phone, BillingCity), Contact(FirstName, LastName, Email), Lead(FirstName, LastName, Company), Opportunity(Name, Amount, StageName)" + + result: List[Dict[str, Any]] = sosl(sosl_query) + return result + + +@retry_on_session_expiration +def list_sobjects(filter: Optional[str] = None) -> List[str]: + """List all available Salesforce objects (sObjects) in the org. + + Args: + filter: Optional fuzzy filter to match object names (case-insensitive substring search). + Examples: "account", "__c" for custom objects, "contact", etc. + + Returns: + list: List of Salesforce object names as strings + """ + sf = _get_salesforce_client() + describe_result = sf.describe() + + if not describe_result: + raise ValueError("Salesforce describe() returned empty result") + + if "sobjects" not in describe_result: + raise ValueError( + f"Unexpected describe response format: missing 'sobjects' field in {describe_result}" + ) + + sobjects = describe_result["sobjects"] + object_names = [] + + for obj in sobjects: + if not isinstance(obj, dict): + raise ValueError(f"Unexpected sobject format: expected dict, got {type(obj)}: {obj}") + if "name" not in obj: + raise ValueError(f"Sobject missing 'name' field: {obj}") + object_names.append(obj["name"]) + + if filter is not None and filter.strip(): + filter_lower = filter.lower() + object_names = [name for name in object_names if filter_lower in name.lower()] + + object_names.sort() + return object_names + + +@retry_on_session_expiration +def describe_sobject(sobject_name: str) -> Dict[str, Any]: + """Get the description of a Salesforce object type. + + Args: + sobject_name: The name of the Salesforce object type + + Returns: + Dictionary containing the object's field descriptions + + Example: + >>> describe_sobject("Account") + """ + logger.info("Describing Salesforce object: %s", sobject_name) + + sf = _get_salesforce_client() + + # Try to get the object - catch this specifically for "object doesn't exist" + try: + sobject = getattr(sf, sobject_name) + except AttributeError: + raise ValueError(f"Salesforce object '{sobject_name}' does not exist") + + # Let API errors from describe() propagate naturally with their original messages + describe_result = sobject.describe() + + if not describe_result: + raise ValueError(f"Salesforce object '{sobject_name}' describe() returned empty result") + + if "fields" not in describe_result: + raise ValueError( + f"Unexpected describe response format for '{sobject_name}': missing 'fields' field in {describe_result}" + ) + + # Process fields into the required format + fields_info = {} + for field in describe_result["fields"]: + if not isinstance(field, dict): + raise ValueError( + f"Unexpected field format in '{sobject_name}': expected dict, got {type(field)}: {field}" + ) + + required_fields = ["name", "type", "label"] + for required_field in required_fields: + if required_field not in field: + raise ValueError(f"Field missing '{required_field}' in '{sobject_name}': {field}") + + field_name = field["name"] + field_info = {"type": field["type"], "label": field["label"]} + + # Add referenceTo information for reference fields + if field["type"] == "reference" and field.get("referenceTo"): + field_info["referenceTo"] = field["referenceTo"] + + fields_info[field_name] = field_info + + return fields_info + + +@retry_on_session_expiration +def get_sobject(sobject_name: str, record_id: str) -> Dict[str, Any]: + """Get a specific Salesforce object by its ID. + + Args: + sobject_name: The name of the Salesforce object type + record_id: The Salesforce ID of the object + + Returns: + Dictionary containing the object's field values + + Example: + >>> get_sobject("Account", "001xx000003DIloAAG") + """ + logger.info("Getting Salesforce object: %s with ID: %s", sobject_name, record_id) + + sf = _get_salesforce_client() + + # Try to get the object - catch this specifically for "object doesn't exist" + try: + sobject = getattr(sf, sobject_name) + except AttributeError: + raise ValueError(f"Salesforce object '{sobject_name}' does not exist") + + result = sobject.get(record_id) + + # Remove 'attributes' field for consistency with other functions + if isinstance(result, dict) and "attributes" in result: + cleaned_result: Dict[str, Any] = {k: v for k, v in result.items() if k != "attributes"} + return cleaned_result + + return dict(result) if result else {} diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/describe_sobject.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/describe_sobject.yml new file mode 100644 index 0000000..bfb9e3e --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/describe_sobject.yml @@ -0,0 +1,27 @@ +mxcp: 1 + +tool: + name: "describe_sobject" + description: "Use this tool to get detailed information about a specific Salesforce object's structure. This is crucial for understanding what fields are available, their types, and their properties. The tool returns comprehensive metadata including field types, picklist values, required fields, and field-level security settings. Use this before querying an object to ensure you're using valid field names and to understand the data types. For example, use this to discover all available fields on an Account or to find the valid values for a picklist field." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: sobject_name + type: string + description: "The API name of the Salesforce object to describe (e.g., 'Account', 'Contact', 'CustomObject__c'). The name is case-sensitive and must match exactly how it appears in Salesforce. You can use list_sobjects() to see all available object names." + examples: + - "Account" + - "Contact" + - "Opportunity" + - "CustomObject__c" + return: + type: object + annotations: + readOnlyHint: true + tests: + - name: "Describe Account object" + description: "Verify Account object description contains expected fields" + arguments: + - key: sobject_name + value: "Account" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/get_sobject.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/get_sobject.yml new file mode 100644 index 0000000..be2ce6b --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/get_sobject.yml @@ -0,0 +1,34 @@ +mxcp: 1 + +tool: + name: "get_sobject" + description: "Use this tool when you have a specific Salesforce record ID and need to retrieve all fields for that record. This is ideal for getting complete details about a known record, like when you have an Account ID from a previous query and need all its information. The tool requires both the object type (e.g., 'Account', 'Contact') and the record's unique ID. This is different from search or SOQL queries which find records based on field values - this tool is for direct record lookup by ID." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: sobject_name + type: string + description: "The API name of the Salesforce object (e.g., 'Account', 'Contact', 'CustomObject__c'). The name is case-sensitive and must match exactly how it appears in Salesforce. You can use list_sobjects() to see all available object names." + examples: + - "Account" + - "Contact" + - "Opportunity" + - name: record_id + type: string + description: "The unique identifier (ID) of the record to retrieve. This is the 15 or 18 character ID assigned by Salesforce when the record is created. You can get these IDs from other queries or from the Salesforce UI." + examples: + - "001xx000003DIloAAG" + - "003xx000004TmiAAE" + return: + type: object + annotations: + readOnlyHint: true + tests: + - name: "Get Account record structure" + description: "Verify Account record has expected fields like Id, Name" + arguments: + - key: sobject_name + value: "Account" + - key: record_id + value: "001Qy00000pxRDKIA2" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/list_sobjects.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/list_sobjects.yml new file mode 100644 index 0000000..9cfeaff --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/list_sobjects.yml @@ -0,0 +1,38 @@ +mxcp: 1 + +tool: + name: "list_sobjects" + description: "Use this tool to get a list of all Salesforce object names available in your org. This is essential for exploring your Salesforce instance and understanding what data you can access. The tool returns a simple list of object names (e.g., ['Account', 'Contact', 'Opportunity']). Use this before using other tools to ensure you're using valid object names. For example, use this to find custom objects in your org or to verify the exact spelling of standard objects. If you need detailed information about a specific object's structure, use the describe_sobject tool instead." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: filter + type: string + description: "Optional fuzzy filter to match object names (case-insensitive substring search). Examples: 'account', '__c' for custom objects, 'contact', etc. If not provided, all objects are returned." + examples: + - "account" + - "__c" + - "contact" + default: null + return: + type: array + items: + type: string + annotations: + readOnlyHint: true + tests: + - name: "Contains standard objects" + description: "Verify standard Salesforce objects are present" + arguments: [] + result_contains_all: + - "Account" + - "Contact" + - "Opportunity" + - name: "filter" + description: "Verify the filter is applied" + arguments: + - key: filter + value: "count" + result_contains_all: + - "Account" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/search.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/search.yml new file mode 100644 index 0000000..d51ff7f --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/search.yml @@ -0,0 +1,28 @@ +mxcp: 1 + +tool: + name: "search" + description: "Use this tool when you want to quickly search for records across multiple Salesforce objects without knowing the exact field names. It's perfect for finding records by company names, people names, or locations. The search is case-insensitive and will match partial words. For example, searching for 'Acme' will find 'Acme Corp', 'Acme Inc', etc. across Account, Contact, Lead, and Opportunity records. If you need more control over which fields to search or which objects to include, use the sosl tool instead." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: search_term + type: string + description: "The term to search for. This will be matched against all searchable fields in the specified objects. For example, searching for 'Acme' will find records containing 'Acme', 'acme', 'ACME', etc." + examples: + - "Acme" + - "John Smith" + - "New York" + return: + type: array + items: + type: object + annotations: + readOnlyHint: true + tests: + - name: "Basic search" + description: "Verify search returns array of results" + arguments: + - key: search_term + value: "Test" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/soql.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/soql.yml new file mode 100644 index 0000000..e51474f --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/soql.yml @@ -0,0 +1,43 @@ +mxcp: 1 + +tool: + name: "soql" + description: "Use this tool when you need to query specific fields from a single Salesforce object, similar to SQL. It's ideal for getting structured data like 'all accounts in New York' or 'all contacts for a specific account'. SOQL is more precise than search or SOSL because you specify exactly which fields you want. For example, use this when you need to get a list of accounts with their phone numbers and addresses, or when you need to find all opportunities over a certain amount. If you need to search across multiple objects or don't know the exact field names, use the search or sosl tools instead." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: query + type: string + description: "The SOQL query to execute. The query should follow SOQL syntax: SELECT field1, field2 FROM object_type [WHERE conditions] [ORDER BY field] [LIMIT n]. For example: 'SELECT Id, Name, Phone FROM Account WHERE BillingCity = 'New York' LIMIT 10'" + examples: + - "SELECT Id, Name FROM Account" + - "SELECT Id, Name, Email FROM Contact WHERE AccountId = '001xx000003DIloAAG'" + - "SELECT Id, Name, Amount, StageName FROM Opportunity WHERE IsWon = true ORDER BY Amount DESC LIMIT 5" + return: + type: array + items: + type: object + annotations: + readOnlyHint: true + tests: + - name: "Basic Account query" + description: "Verify SOQL query returns array of Account records" + arguments: + - key: query + value: "SELECT Id, Name FROM Account LIMIT 1" + - name: "Query with LIMIT" + description: "Verify SOQL query respects LIMIT clause" + arguments: + - key: query + value: "SELECT Id FROM Account LIMIT 2" + - name: "Contact query structure" + description: "Verify Contact query returns expected fields" + arguments: + - key: query + value: "SELECT Id, FirstName, LastName FROM Contact LIMIT 1" + - name: "Results should not contain attributes" + description: "Verify attributes field is filtered out from results" + arguments: + - key: query + value: "SELECT Id, Name FROM Account LIMIT 1" diff --git a/skills/mxcp-expert/assets/project-templates/salesforce/tools/sosl.yml b/skills/mxcp-expert/assets/project-templates/salesforce/tools/sosl.yml new file mode 100644 index 0000000..5b00d09 --- /dev/null +++ b/skills/mxcp-expert/assets/project-templates/salesforce/tools/sosl.yml @@ -0,0 +1,43 @@ +mxcp: 1 + +tool: + name: "sosl" + description: "Use this tool when you need advanced search capabilities across multiple Salesforce objects. It's perfect for complex search scenarios like 'find all records containing this text in any field' or 'search only in name fields across accounts and contacts'. SOSL gives you fine-grained control over which objects to search and which fields to return. For example, use this when you need to find all records mentioning a specific product across accounts, contacts, and opportunities. If you just need a simple search, use the search tool instead. If you need to query specific fields from a single object, use the soql tool instead." + language: python + source: + file: ../python/salesforce_endpoints.py + parameters: + - name: query + type: string + description: "The SOSL query to execute. The query should follow SOSL syntax: FIND {search_term} IN ALL FIELDS RETURNING object_type(field1, field2, ...). For example: 'FIND {Acme} IN ALL FIELDS RETURNING Account(Name, Phone), Contact(FirstName, LastName)'" + examples: + - "FIND {Acme} IN ALL FIELDS RETURNING Account(Name, Phone)" + - "FIND {John} IN NAME FIELDS RETURNING Contact(FirstName, LastName, Email)" + - "FIND {New York} IN ALL FIELDS RETURNING Account(Name, BillingCity), Lead(Company, City)" + return: + type: array + items: + type: object + annotations: + readOnlyHint: true + tests: + - name: "Basic SOSL search" + description: "Verify SOSL search returns array of search results" + arguments: + - key: query + value: "FIND {Test} IN ALL FIELDS RETURNING Account(Name)" + - name: "Multi-object search" + description: "Verify SOSL can search across multiple objects" + arguments: + - key: query + value: "FIND {Test} IN ALL FIELDS RETURNING Account(Name), Contact(FirstName, LastName)" + - name: "Name field search" + description: "Verify SOSL can search in specific fields" + arguments: + - key: query + value: "FIND {Test} IN NAME FIELDS RETURNING Account(Name)" + - name: "Search with specific fields" + description: "Verify SOSL returns specified fields" + arguments: + - key: query + value: "FIND {Test} IN ALL FIELDS RETURNING Account(Name, Phone)" diff --git a/skills/mxcp-expert/assets/project-templates/squirro/data/db-default.duckdb b/skills/mxcp-expert/assets/project-templates/squirro/data/db-default.duckdb new file mode 100644 index 0000000000000000000000000000000000000000..88ec05a2717ff0b85415f32f058b8d67d4e1929f GIT binary patch literal 12288 zcmeI#u?fOZ5CG6Gh`5A}Z6F#VhExjL33k?^?qKB#LYA;|4>xcS7tkUE!4R-Yns?0| zcLxWryTRx&iJtdjy<2UT;lDU1@hnc7caLd4J!Gden>X*65FkK+009C72oNAZfB*pk z?Frmo*UNq}-c}diKT-8V=$GZNOug@*R{8&HBpL(=5FkK+009C72oNAZpi=?=f2aJ8 V6Cgl<009C72oNAZfB=D31U|{RHAesd literal 0 HcmV?d00001 diff --git a/skills/mxcp-expert/assets/schemas/common-types-schema-1.json b/skills/mxcp-expert/assets/schemas/common-types-schema-1.json new file mode 100644 index 0000000..ea52dbf --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/common-types-schema-1.json @@ -0,0 +1,171 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Common Type Definitions", + "description": "Common type definitions shared across MXCP tool, resource, and prompt schemas", + + "definitions": { + "typeDefinition": { + "type": "object", + "required": ["type"], + "properties": { + "type": { + "type": "string", + "enum": ["string", "number", "integer", "boolean", "array", "object"], + "description": "The data type of the value." + }, + "format": { + "type": "string", + "enum": ["email", "uri", "date", "time", "date-time", "duration", "timestamp"], + "description": "Expected format for string values." + }, + "sensitive": { + "type": "boolean", + "description": "Whether this field contains sensitive data that should be redacted in logs and filtered by policies.", + "default": false + }, + "minLength": { + "type": "integer", + "minimum": 0, + "description": "Minimum string length." + }, + "maxLength": { + "type": "integer", + "minimum": 0, + "description": "Maximum string length." + }, + "minimum": { + "type": "number", + "description": "Minimum value for numbers or integers." + }, + "maximum": { + "type": "number", + "description": "Maximum value for numbers or integers." + }, + "exclusiveMinimum": { + "type": "number", + "description": "Exclusive minimum value for numbers or integers." + }, + "exclusiveMaximum": { + "type": "number", + "description": "Exclusive maximum value for numbers or integers." + }, + "multipleOf": { + "type": "number", + "description": "Value must be a multiple of this for numbers or integers." + }, + "minItems": { + "type": "integer", + "minimum": 0, + "description": "Minimum number of array items." + }, + "maxItems": { + "type": "integer", + "minimum": 0, + "description": "Maximum number of array items." + }, + "uniqueItems": { + "type": "boolean", + "description": "Whether array items must be unique." + }, + "items": { + "$ref": "#/definitions/typeDefinition", + "description": "Schema for items if type is array." + }, + "properties": { + "type": "object", + "description": "Schema for object properties if type is object.", + "additionalProperties": { + "$ref": "#/definitions/typeDefinition" + } + }, + "required": { + "type": "array", + "description": "List of required fields if type is object.", + "items": { "type": "string" } + }, + "additionalProperties": { + "type": "boolean", + "description": "Whether to allow additional properties not defined in the schema. Defaults to true if not specified." + } + } + }, + + "paramDefinition": { + "type": "object", + "required": ["name", "type", "description"], + "properties": { + "name": { + "type": "string", + "description": "Parameter name.", + "pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$", + "minLength": 1 + }, + "description": { + "type": "string", + "description": "What this parameter represents." + }, + "default": { + "description": "Optional default value if none is provided." + }, + "examples": { + "type": "array", + "description": "Example values for this parameter.", + "items": {} + }, + "enum": { + "type": "array", + "description": "List of allowed values.", + "items": {} + } + }, + "allOf": [ + { "$ref": "#/definitions/typeDefinition" } + ] + }, + + "policySet": { + "type": "object", + "description": "Policy definitions for endpoint access control and data filtering.", + "properties": { + "input": { + "type": "array", + "description": "Input policies evaluated before endpoint execution.", + "items": { "$ref": "#/definitions/policyDefinition" } + }, + "output": { + "type": "array", + "description": "Output policies evaluated after endpoint execution.", + "items": { "$ref": "#/definitions/policyDefinition" } + } + }, + "additionalProperties": false + }, + + "policyDefinition": { + "type": "object", + "required": ["condition", "action"], + "description": "A single policy rule definition.", + "properties": { + "condition": { + "type": "string", + "description": "CEL expression that determines when this policy applies." + }, + "action": { + "type": "string", + "enum": ["deny", "filter_fields", "mask_fields", "filter_sensitive_fields"], + "description": "Action to take when the condition is true." + }, + "reason": { + "type": "string", + "description": "Human-readable explanation for the policy action." + }, + "fields": { + "type": "array", + "items": { "type": "string" }, + "description": "List of field names for filter_fields and mask_fields actions." + } + }, + "additionalProperties": false + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/drift-report-schema-1.json b/skills/mxcp-expert/assets/schemas/drift-report-schema-1.json new file mode 100644 index 0000000..56123ef --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/drift-report-schema-1.json @@ -0,0 +1,145 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Drift Report", + "type": "object", + "required": ["version", "generated_at", "baseline_snapshot_path", "current_snapshot_generated_at", "baseline_snapshot_generated_at", "has_drift", "summary", "table_changes", "resource_changes"], + "properties": { + "version": { + "type": "integer", + "description": "Version of the drift report format. Must be 1.", + "enum": [1], + "default": 1 + }, + "generated_at": { + "type": "string", + "format": "date-time", + "description": "ISO 8601 timestamp when the report was generated" + }, + "baseline_snapshot_path": { + "type": "string", + "description": "Path to the baseline snapshot file" + }, + "current_snapshot_generated_at": { + "type": "string", + "format": "date-time", + "description": "Timestamp when the current snapshot was generated" + }, + "baseline_snapshot_generated_at": { + "type": "string", + "format": "date-time", + "description": "Timestamp when the baseline snapshot was generated" + }, + "has_drift": { + "type": "boolean", + "description": "Whether any drift was detected" + }, + "summary": { + "type": "object", + "description": "Summary counts of changes by type", + "properties": { + "tables_added": { "type": "integer" }, + "tables_removed": { "type": "integer" }, + "tables_modified": { "type": "integer" }, + "resources_added": { "type": "integer" }, + "resources_removed": { "type": "integer" }, + "resources_modified": { "type": "integer" } + }, + "additionalProperties": false + }, + "table_changes": { + "type": "array", + "description": "List of table changes detected", + "items": { + "type": "object", + "required": ["name", "change_type"], + "properties": { + "name": { + "type": "string", + "description": "Name of the table" + }, + "change_type": { + "type": "string", + "enum": ["added", "removed", "modified"], + "description": "Type of change" + }, + "columns_added": { + "type": "array", + "description": "Columns that were added", + "items": { + "type": "object", + "required": ["name", "type"], + "properties": { + "name": { "type": "string" }, + "type": { "type": "string" } + } + } + }, + "columns_removed": { + "type": "array", + "description": "Columns that were removed", + "items": { + "type": "object", + "required": ["name", "type"], + "properties": { + "name": { "type": "string" }, + "type": { "type": "string" } + } + } + }, + "columns_modified": { + "type": "array", + "description": "Columns that were modified", + "items": { + "type": "object", + "properties": { + "name": { "type": "string" }, + "old_type": { "type": "string" }, + "new_type": { "type": "string" } + } + } + } + } + } + }, + "resource_changes": { + "type": "array", + "description": "List of resource changes detected", + "items": { + "type": "object", + "required": ["path", "change_type"], + "properties": { + "path": { + "type": "string", + "description": "Path to the resource file" + }, + "endpoint": { + "type": "string", + "description": "Endpoint identifier (e.g., 'tool/name')" + }, + "change_type": { + "type": "string", + "enum": ["added", "removed", "modified"], + "description": "Type of change" + }, + "validation_changed": { + "type": "boolean", + "description": "Whether validation results changed" + }, + "test_results_changed": { + "type": "boolean", + "description": "Whether test results changed" + }, + "definition_changed": { + "type": "boolean", + "description": "Whether endpoint definition changed" + }, + "details": { + "type": "object", + "description": "Specific details about what changed", + "additionalProperties": true + } + } + } + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/drift-snapshot-schema-1.json b/skills/mxcp-expert/assets/schemas/drift-snapshot-schema-1.json new file mode 100644 index 0000000..8b72fc3 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/drift-snapshot-schema-1.json @@ -0,0 +1,145 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Drift Snapshot", + "type": "object", + "required": ["version", "generated_at", "tables", "resources"], + "properties": { + "version": { + "type": "integer", + "description": "Version of the drift snapshot format. Must be 1.", + "enum": [1], + "default": 1 + }, + "generated_at": { + "type": "string", + "format": "date-time", + "description": "ISO 8601 timestamp when the snapshot was generated" + }, + + "tables": { + "type": "array", + "description": "List of tables in the DuckDB catalog", + "items": { + "type": "object", + "required": ["name", "columns"], + "properties": { + "name": { + "type": "string", + "description": "Name of the table" + }, + "columns": { + "type": "array", + "description": "List of columns in the table", + "items": { + "type": "object", + "required": ["name", "type"], + "properties": { + "name": { + "type": "string", + "description": "Name of the column" + }, + "type": { + "type": "string", + "description": "DuckDB data type of the column" + } + } + } + } + } + } + }, + "resources": { + "type": "array", + "description": "List of resources with validation results, test results, and definition", + "items": { + "type": "object", + "required": ["validation_results"], + "properties": { + "validation_results": { + "type": "object", + "required": ["status", "path"], + "properties": { + "status": { + "type": "string", + "enum": ["ok", "error"], + "description": "Validation status" + }, + "path": { + "type": "string", + "description": "Path to the endpoint file (relative to repository root)" + }, + "message": { + "type": "string", + "description": "Error message if validation failed" + } + }, + "description": "Results of validation for the resource" + }, + "test_results": { + "type": "object", + "required": ["status", "tests_run"], + "properties": { + "status": { + "type": "string", + "enum": ["ok", "error", "failed"], + "description": "Test execution status" + }, + "tests_run": { + "type": "integer", + "description": "Number of tests run" + }, + "tests": { + "type": "array", + "description": "List of per-test results", + "items": { + "type": "object", + "required": ["name", "status", "time"], + "properties": { + "name": { + "type": "string", + "description": "Name of the test" + }, + "description": { + "type": "string", + "description": "Description of the test" + }, + "status": { + "type": "string", + "enum": ["passed", "failed", "error"], + "description": "Test status" + }, + "error": { + "type": "string", + "description": "Error message if test failed" + }, + "time": { + "type": "number", + "description": "Time taken to run the test in seconds" + } + } + } + } + }, + "description": "Results of tests for the resource" + }, + "definition": { + "oneOf": [ + { "$ref": "../../endpoints/endpoint_schemas/tool-schema-1.json#/definitions/toolDefinition" }, + { "$ref": "../../endpoints/endpoint_schemas/resource-schema-1.json#/definitions/resourceDefinition" }, + { "$ref": "../../endpoints/endpoint_schemas/prompt-schema-1.json#/definitions/promptDefinition" } + ], + "description": "Endpoint definition" + }, + "metadata": { + "type": "object", + "properties": { + "title": { "type": "string", "description": "Short display title" }, + "description": { "type": "string", "description": "Longer description" } + }, + "description": "Optional metadata for documentation purposes" + } + } + } + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/eval-schema-1.json b/skills/mxcp-expert/assets/schemas/eval-schema-1.json new file mode 100644 index 0000000..8754a6f --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/eval-schema-1.json @@ -0,0 +1,111 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Eval Suite", + "type": "object", + "required": ["mxcp", "suite", "tests"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Schema version. Must be 1.", + "enum": [1], + "default": 1 + }, + "suite": { + "type": "string", + "description": "Name of the eval suite (e.g., 'churn_checks')", + "pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$" + }, + "description": { + "type": "string", + "description": "Description of what this eval suite tests" + }, + "model": { + "type": "string", + "description": "Optional model to use for this suite (e.g., 'claude-4-opus')", + "enum": [ + "claude-4-opus", + "claude-4-sonnet", + "gpt-4o", + "gpt-4.1" + ] + }, + "tests": { + "type": "array", + "description": "List of eval tests to run", + "items": { + "type": "object", + "required": ["name", "prompt", "assertions"], + "properties": { + "name": { + "type": "string", + "description": "Name of the test", + "pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$" + }, + "description": { + "type": "string", + "description": "What this test is checking" + }, + "prompt": { + "type": "string", + "description": "The prompt to send to the LLM" + }, + "user_context": { + "type": "object", + "description": "Optional user context for this test (e.g., role, permissions)", + "additionalProperties": true + }, + "assertions": { + "type": "object", + "description": "Assertions to validate the LLM's response", + "properties": { + "must_call": { + "type": "array", + "description": "Tools that must be called with specific arguments", + "items": { + "type": "object", + "required": ["tool", "args"], + "properties": { + "tool": { + "type": "string", + "description": "Name of the tool that must be called" + }, + "args": { + "type": "object", + "description": "Expected arguments for the tool call", + "additionalProperties": true + } + }, + "additionalProperties": false + } + }, + "must_not_call": { + "type": "array", + "description": "List of tool names that should NOT be called", + "items": { + "type": "string" + } + }, + "answer_contains": { + "type": "array", + "description": "Strings that must appear in the LLM's answer", + "items": { + "type": "string" + } + }, + "answer_not_contains": { + "type": "array", + "description": "Strings that must NOT appear in the LLM's answer", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + } + }, + "additionalProperties": false +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/mxcp-config-schema-1.json b/skills/mxcp-expert/assets/schemas/mxcp-config-schema-1.json new file mode 100644 index 0000000..3551df4 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/mxcp-config-schema-1.json @@ -0,0 +1,585 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Config", + "type": "object", + "required": ["mxcp", "projects"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Schema version. Must be 1.", + "enum": [1], + "default": 1 + }, + "vault": { + "type": "object", + "description": "Configuration for Vault integration.", + "required": ["enabled"], + "properties": { + "enabled": { "type": "boolean" }, + "address": { + "type": "string", + "format": "uri" + }, + "token_env": { + "type": "string", + "description": "The environment variable name containing the Vault token." + } + }, + "additionalProperties": false + }, + "onepassword": { + "type": "object", + "description": "Configuration for 1Password integration using service account.", + "required": ["enabled"], + "properties": { + "enabled": { "type": "boolean" }, + "token_env": { + "type": "string", + "description": "The environment variable name containing the 1Password service account token.", + "default": "OP_SERVICE_ACCOUNT_TOKEN" + } + }, + "additionalProperties": false + }, + "transport": { + "type": "object", + "description": "Default transport configuration for serving endpoints.", + "properties": { + "provider": { + "type": "string", + "enum": ["streamable-http", "sse", "stdio"], + "default": "streamable-http", + "description": "Default transport protocol to use." + }, + "http": { + "type": "object", + "description": "HTTP transport specific configuration.", + "properties": { + "port": { + "type": "integer", + "minimum": 1, + "maximum": 65535, + "default": 8000, + "description": "Default port number for HTTP transport." + }, + "host": { + "type": "string", + "default": "localhost", + "description": "Default host to bind the HTTP server to." + }, + "scheme": { + "type": "string", + "enum": ["http", "https"], + "default": "http", + "description": "URL scheme to use for generating callback URLs and OAuth endpoints. Use 'https' when behind SSL-terminating reverse proxy." + }, + "base_url": { + "type": "string", + "format": "uri", + "description": "Complete base URL for the server (e.g., 'https://api.example.com'). When provided, overrides scheme, host, and port for URL generation." + }, + "trust_proxy": { + "type": "boolean", + "default": false, + "description": "Whether to trust X-Forwarded-* headers from reverse proxies for scheme detection." + }, + "stateless": { + "type": "boolean", + "default": false, + "description": "Enable stateless HTTP mode for serverless deployments. In stateless mode, no session state is maintained between requests." + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + }, + "models": { + "type": "object", + "description": "Configuration for LLM models used in evals.", + "properties": { + "default": { + "type": "string", + "description": "Default model to use when not specified in eval suite.", + "enum": ["claude-4-sonnet", "claude-4-opus", "gpt-4o", "gpt-4.1"] + }, + "models": { + "type": "object", + "description": "Model-specific configurations.", + "patternProperties": { + "^(claude-4-sonnet|claude-4-opus|gpt-4o|gpt-4\\.1)$": { + "type": "object", + "required": ["type"], + "properties": { + "type": { + "type": "string", + "enum": ["claude", "openai"], + "description": "Provider type for this model." + }, + "api_key": { + "type": "string", + "description": "API key for this model." + }, + "base_url": { + "type": "string", + "format": "uri", + "description": "Custom API endpoint URL." + }, + "timeout": { + "type": "integer", + "minimum": 1, + "default": 30, + "description": "Request timeout in seconds." + }, + "max_retries": { + "type": "integer", + "minimum": 0, + "default": 3, + "description": "Maximum number of retries for failed requests." + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + }, + "projects": { + "type": "object", + "description": "All configured RAW projects.", + "patternProperties": { + "^[a-zA-Z0-9_-]+$": { + "type": "object", + "required": ["profiles"], + "properties": { + "profiles": { + "type": "object", + "description": "Profiles under this project.", + "patternProperties": { + "^[a-zA-Z0-9_-]+$": { + "type": "object", + "properties": { + "secrets": { + "type": "array", + "items": { + "type": "object", + "required": ["name", "type", "parameters"], + "properties": { + "name": { "type": "string", "description": "The secret name." }, + "type": { "type": "string", "description": "The secret type." }, + "parameters": { + "type": "object", + "description": "The secret definition.", + "additionalProperties": { + "oneOf": [ + { "type": "string" }, + { + "type": "object", + "additionalProperties": { "type": "string" } + } + ] + } + } + }, + "additionalProperties": false + } + }, + "telemetry": { + "type": "object", + "description": "Unified telemetry configuration for traces, metrics, and logs.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Global telemetry enable/disable.", + "default": false + }, + "endpoint": { + "type": "string", + "description": "OTLP endpoint URL (e.g., http://localhost:4318).", + "format": "uri" + }, + "headers": { + "type": "object", + "description": "Additional headers for the OTLP exporter.", + "additionalProperties": { + "type": "string" + } + }, + "service_name": { + "type": "string", + "description": "Override the default service name (mxcp)." + }, + "service_version": { + "type": "string", + "description": "Service version string." + }, + "environment": { + "type": "string", + "description": "Deployment environment (e.g., production, staging)." + }, + "resource_attributes": { + "type": "object", + "description": "Additional resource attributes for all telemetry.", + "additionalProperties": { + "type": "string" + } + }, + "tracing": { + "type": "object", + "description": "Distributed tracing configuration.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Whether tracing is enabled.", + "default": true + }, + "console_export": { + "type": "boolean", + "description": "Export spans to console for debugging.", + "default": false + } + }, + "additionalProperties": false + }, + "metrics": { + "type": "object", + "description": "Metrics collection configuration.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Whether metrics collection is enabled.", + "default": true + }, + "export_interval": { + "type": "integer", + "description": "Export interval in seconds.", + "default": 60, + "minimum": 1 + }, + "prometheus_port": { + "type": "integer", + "description": "Optional port for Prometheus scraping endpoint.", + "minimum": 1024, + "maximum": 65535 + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + }, + "plugin": { + "type": "object", + "description": "Plugin configuration for this profile.", + "properties": { + "config": { + "type": "object", + "description": "Plugin-specific configurations.", + "patternProperties": { + "^[a-zA-Z0-9_-]+$": { + "type": "object", + "description": "Configuration for a specific plugin.", + "additionalProperties": { + "type": "string" + } + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + }, + "auth": { + "type": "object", + "description": "Authentication configuration for this profile", + "properties": { + "provider": { + "type": "string", + "enum": ["none", "github", "atlassian", "salesforce", "keycloak", "google"], + "default": "none", + "description": "OAuth provider to use for authentication" + }, + "authorization": { + "type": "object", + "description": "Authorization settings for controlling access to MCP functionality", + "properties": { + "required_scopes": { + "type": "array", + "items": {"type": "string"}, + "default": [], + "description": "List of scopes required to access any MCP endpoint. Empty list means no scopes required (authentication only)." + } + } + }, + "clients": { + "type": "array", + "description": "Pre-registered OAuth clients for development and testing.", + "items": { + "type": "object", + "required": ["client_id", "name"], + "properties": { + "client_id": { + "type": "string", + "description": "OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "OAuth client secret (optional for public clients)." + }, + "name": { + "type": "string", + "description": "Human-readable name for this client." + }, + "redirect_uris": { + "type": "array", + "items": { + "type": "string", + "format": "uri" + }, + "description": "Allowed redirect URIs for this client.", + "default": ["http://127.0.0.1:49153/oauth/callback"] + }, + "grant_types": { + "type": "array", + "items": { + "type": "string", + "enum": ["authorization_code", "refresh_token"] + }, + "description": "Allowed OAuth grant types.", + "default": ["authorization_code"] + }, + "scopes": { + "type": "array", + "items": { + "type": "string" + }, + "description": "Allowed OAuth scopes.", + "default": ["mxcp:access"] + } + }, + "additionalProperties": false + } + }, + "github": { + "type": "object", + "description": "GitHub OAuth configuration (required when provider is 'github').", + "required": ["client_id", "client_secret", "callback_path", "auth_url", "token_url"], + "properties": { + "client_id": { + "type": "string", + "description": "GitHub OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "GitHub OAuth client secret." + }, + "scope": { + "type": "string", + "description": "OAuth scope to request (optional).", + "default": "user:email" + }, + "callback_path": { + "type": "string", + "description": "Callback path for OAuth flow.", + "default": "/github/callback" + }, + "auth_url": { + "type": "string", + "format": "uri", + "description": "GitHub authorization URL." + }, + "token_url": { + "type": "string", + "format": "uri", + "description": "GitHub token exchange URL." + } + }, + "additionalProperties": false + }, + "atlassian": { + "type": "object", + "description": "Atlassian OAuth configuration (required when provider is 'atlassian').", + "required": ["client_id", "client_secret", "callback_path", "auth_url", "token_url"], + "properties": { + "client_id": { + "type": "string", + "description": "Atlassian OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "Atlassian OAuth client secret." + }, + "scope": { + "type": "string", + "description": "OAuth scopes to request (space-separated).", + "default": "read:jira-work read:jira-user read:confluence-content.all read:confluence-user offline_access" + }, + "callback_path": { + "type": "string", + "description": "Callback path for OAuth flow.", + "default": "/atlassian/callback" + }, + "auth_url": { + "type": "string", + "format": "uri", + "description": "Atlassian authorization URL.", + "default": "https://auth.atlassian.com/authorize" + }, + "token_url": { + "type": "string", + "format": "uri", + "description": "Atlassian token exchange URL.", + "default": "https://auth.atlassian.com/oauth/token" + } + }, + "additionalProperties": false + }, + "salesforce": { + "type": "object", + "description": "Salesforce OAuth configuration (required when provider is 'salesforce').", + "required": ["client_id", "client_secret", "callback_path", "auth_url", "token_url"], + "properties": { + "client_id": { + "type": "string", + "description": "Salesforce OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "Salesforce OAuth client secret." + }, + "scope": { + "type": "string", + "description": "OAuth scopes to request (space-separated).", + "default": "api refresh_token openid profile email" + }, + "callback_path": { + "type": "string", + "description": "Callback path for OAuth flow.", + "default": "/salesforce/callback" + }, + "auth_url": { + "type": "string", + "format": "uri", + "description": "Salesforce authorization URL.", + "default": "https://login.salesforce.com/services/oauth2/authorize" + }, + "token_url": { + "type": "string", + "format": "uri", + "description": "Salesforce token exchange URL.", + "default": "https://login.salesforce.com/services/oauth2/token" + } + }, + "additionalProperties": false + }, + "keycloak": { + "type": "object", + "description": "Keycloak OAuth configuration (required when provider is 'keycloak').", + "required": ["client_id", "client_secret", "realm", "server_url"], + "properties": { + "client_id": { + "type": "string", + "description": "Keycloak OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "Keycloak OAuth client secret." + }, + "realm": { + "type": "string", + "description": "Keycloak realm name." + }, + "server_url": { + "type": "string", + "format": "uri", + "description": "Keycloak server base URL (e.g., 'http://localhost:8080')." + }, + "scope": { + "type": "string", + "description": "OAuth scopes to request (space-separated).", + "default": "openid profile email" + }, + "callback_path": { + "type": "string", + "description": "Callback path for OAuth flow.", + "default": "/keycloak/callback" + } + }, + "additionalProperties": false + }, + "google": { + "type": "object", + "description": "Google OAuth configuration (required when provider is 'google').", + "required": ["client_id", "client_secret", "callback_path", "auth_url", "token_url"], + "properties": { + "client_id": { + "type": "string", + "description": "Google OAuth client ID." + }, + "client_secret": { + "type": "string", + "description": "Google OAuth client secret." + }, + "scope": { + "type": "string", + "description": "OAuth scopes to request (space-separated).", + "default": "https://www.googleapis.com/auth/calendar.readonly openid profile email" + }, + "callback_path": { + "type": "string", + "description": "Callback path for OAuth flow.", + "default": "/google/callback" + }, + "auth_url": { + "type": "string", + "format": "uri", + "description": "Google authorization URL.", + "default": "https://accounts.google.com/o/oauth2/v2/auth" + }, + "token_url": { + "type": "string", + "format": "uri", + "description": "Google token exchange URL.", + "default": "https://oauth2.googleapis.com/token" + } + }, + "additionalProperties": false + }, + "persistence": { + "type": "object", + "description": "OAuth state persistence configuration for maintaining authentication state across server restarts.", + "properties": { + "type": { + "type": "string", + "enum": ["sqlite"], + "default": "sqlite", + "description": "Type of persistence backend to use." + }, + "path": { + "type": "string", + "description": "Path to the SQLite database file for storing OAuth state.", + "default": "~/.mxcp/oauth.db" + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false +} diff --git a/skills/mxcp-expert/assets/schemas/mxcp-site-schema-1.json b/skills/mxcp-expert/assets/schemas/mxcp-site-schema-1.json new file mode 100644 index 0000000..d86a539 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/mxcp-site-schema-1.json @@ -0,0 +1,270 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Site Config (mxcp-site.yml)", + "type": "object", + "required": ["mxcp", "project", "profile"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Version of the mxcp-site.yml format. Must be 1.", + "enum": [1], + "default": 1 + }, + "project": { + "type": "string", + "description": "Project name (must match one in ~/.mxcp/config.yml)." + }, + "profile": { + "type": "string", + "description": "Profile name under the given project." + }, + "secrets": { + "type": "array", + "description": "List of secret names used by this repo (resolved from ~/.mxcp/config.yml).", + "items": { + "type": "string" + } + }, + "plugin": { + "type": "array", + "description": "List of plugin modules to load and their configurations.", + "items": { + "type": "object", + "required": ["name", "module"], + "properties": { + "name": { + "type": "string", + "description": "The name of the plugin instance." + }, + "module": { + "type": "string", + "description": "The Python module containing the MXCP plugin." + }, + "config": { + "type": "string", + "description": "Optional name of the configuration to use from the user config (resolved from ~/.mxcp/config.yml)." + } + }, + "additionalProperties": false + } + }, + "extensions": { + "type": "array", + "description": "List of DuckDB extensions to load. Can be simple strings for core extensions or objects with name/repo for community/nightly extensions.", + "items": { + "oneOf": [ + { + "type": "string", + "description": "Name of a core DuckDB extension" + }, + { + "type": "object", + "required": ["name"], + "properties": { + "name": { + "type": "string", + "description": "Name of the extension" + }, + "repo": { + "type": "string", + "description": "Repository to load the extension from (e.g., 'community', 'core_nightly')", + "enum": ["community", "core_nightly"] + } + }, + "additionalProperties": false + } + ] + } + }, + "dbt": { + "type": "object", + "description": "Controls dbt integration and file paths.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Whether to use dbt in this repo (defaults to true)." + }, + "model_paths": { + "type": "array", + "description": "Paths to dbt model directories (defaults to ['models']).", + "items": { + "type": "string" + } + }, + "analysis_paths": { + "type": "array", + "description": "Paths to dbt analysis directories (defaults to ['analyses']).", + "items": { + "type": "string" + } + }, + "test_paths": { + "type": "array", + "description": "Paths to dbt test directories (defaults to ['tests']).", + "items": { + "type": "string" + } + }, + "seed_paths": { + "type": "array", + "description": "Paths to dbt seed directories (defaults to ['seeds']).", + "items": { + "type": "string" + } + }, + "macro_paths": { + "type": "array", + "description": "Paths to dbt macro directories (defaults to ['macros']).", + "items": { + "type": "string" + } + }, + "snapshot_paths": { + "type": "array", + "description": "Paths to dbt snapshot directories (defaults to ['snapshots']).", + "items": { + "type": "string" + } + }, + "target_path": { + "type": "string", + "description": "Path to dbt target directory (defaults to 'target')." + }, + "clean_targets": { + "type": "array", + "description": "Paths to clean when running dbt clean (defaults to ['target', 'dbt_packages']).", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false + }, + "profiles": { + "type": "object", + "description": "Profile-specific configuration settings.", + "patternProperties": { + "^[a-zA-Z0-9_-]+$": { + "type": "object", + "properties": { + "duckdb": { + "type": "object", + "description": "Profile-specific DuckDB configuration.", + "properties": { + "path": { + "type": "string", + "description": "Path to the DuckDB file for this profile." + }, + "readonly": { + "type": "boolean", + "description": "Whether to open the DuckDB connection in read-only mode (defaults to false).", + "default": false + } + }, + "additionalProperties": false + }, + "drift": { + "type": "object", + "description": "Profile-specific MXCP schema drift detection manifest configuration.", + "properties": { + "path": { + "type": "string", + "description": "Path to the MXCP drift manifest file (JSON) for this profile." + } + }, + "additionalProperties": false + }, + "audit": { + "type": "object", + "description": "Profile-specific audit logging configuration.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Whether to enable audit logging for this profile (defaults to false).", + "default": false + }, + "path": { + "type": "string", + "description": "Path to the audit log JSONL file for this profile." + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + }, + "sql_tools": { + "type": "object", + "description": "Configuration for built-in SQL querying and schema exploration tools.", + "properties": { + "enabled": { + "type": "boolean", + "description": "Whether to enable built-in SQL querying and schema exploration tools (defaults to false).", + "default": false + } + }, + "additionalProperties": false + }, + "paths": { + "type": "object", + "description": "Directory paths for different types of MXCP components.", + "properties": { + "tools": { + "type": "string", + "description": "Directory path for tool definitions (defaults to 'tools').", + "default": "tools" + }, + "resources": { + "type": "string", + "description": "Directory path for resource definitions (defaults to 'resources').", + "default": "resources" + }, + "prompts": { + "type": "string", + "description": "Directory path for prompt definitions (defaults to 'prompts').", + "default": "prompts" + }, + "evals": { + "type": "string", + "description": "Directory path for evaluation definitions (defaults to 'evals').", + "default": "evals" + }, + "python": { + "type": "string", + "description": "Directory path for Python extensions and shared code (defaults to 'python').", + "default": "python" + }, + "plugins": { + "type": "string", + "description": "Directory path for MXCP plugins (defaults to 'plugins').", + "default": "plugins" + }, + "sql": { + "type": "string", + "description": "Directory path for SQL files (defaults to 'sql').", + "default": "sql" + }, + "drift": { + "type": "string", + "description": "Directory path for drift snapshots (defaults to 'drift').", + "default": "drift" + }, + "audit": { + "type": "string", + "description": "Directory path for audit logs (defaults to 'audit').", + "default": "audit" + }, + "data": { + "type": "string", + "description": "Directory path for data files including DuckDB databases (defaults to 'data').", + "default": "data" + } + }, + "additionalProperties": false + } + }, + "additionalProperties": false +} diff --git a/skills/mxcp-expert/assets/schemas/prompt-schema-1.json b/skills/mxcp-expert/assets/schemas/prompt-schema-1.json new file mode 100644 index 0000000..9b012b9 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/prompt-schema-1.json @@ -0,0 +1,76 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Prompt Definition Schema", + "type": "object", + "required": ["mxcp", "prompt"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Schema version. Must be 1.", + "enum": [1], + "default": 1 + }, + + "prompt": { + "$ref": "#/definitions/promptDefinition", + "description": "Defines an MCP prompt endpoint." + }, + + "metadata": { + "type": "object", + "properties": { + "title": { "type": "string", "description": "Short display title." }, + "description": { "type": "string", "description": "Longer description." } + }, + "description": "Optional metadata for documentation purposes." + } + }, + + "definitions": { + "promptDefinition": { + "type": "object", + "required": ["name"], + "properties": { + "name": { + "type": "string", + "description": "Logical name identifying this prompt.", + "minLength": 1 + }, + "description": { "type": "string", "description": "Description of this prompt." }, + "tags": { + "type": "array", + "items": { "type": "string" }, + "description": "Tags to classify the prompt." + }, + "parameters": { + "type": "array", + "description": "Input parameters used to populate the prompt.", + "items": { "$ref": "common-types-schema-1.json#/definitions/paramDefinition" } + }, + "messages": { + "type": "array", + "description": "List of structured prompt messages forming the full prompt sequence.", + "items": { + "type": "object", + "required": ["prompt"], + "properties": { + "role": { + "type": "string", + "description": "The role of the speaker of the message (e.g. 'user', 'assistant', 'system')." + }, + "type": { + "type": "string", + "description": "The content type of the message (e.g. 'text')." + }, + "prompt": { + "type": "string", + "description": "The templated prompt text (supports Jinja syntax)." + } + }, + "additionalProperties": false + } + } + } + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/resource-schema-1.json b/skills/mxcp-expert/assets/schemas/resource-schema-1.json new file mode 100644 index 0000000..7fc5dd9 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/resource-schema-1.json @@ -0,0 +1,149 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Resource Definition Schema", + "type": "object", + "required": ["mxcp", "resource"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Schema version. Must be 1.", + "enum": [1], + "default": 1 + }, + + "resource": { + "$ref": "#/definitions/resourceDefinition", + "description": "Defines an MCP resource endpoint." + }, + + "metadata": { + "type": "object", + "properties": { + "title": { "type": "string", "description": "Short display title." }, + "description": { "type": "string", "description": "Longer description." } + }, + "description": "Optional metadata for documentation purposes." + } + }, + + "definitions": { + "resourceDefinition": { + "type": "object", + "required": ["uri", "source"], + "properties": { + "uri": { + "type": "string", + "description": "Logical URI identifying this resource.", + "pattern": "^[A-Za-z][A-Za-z0-9+.-]*://(?:[A-Za-z0-9._-]+|\\{[A-Za-z_][A-Za-z0-9_]*\\})(?:/(?:[A-Za-z0-9._-]+|\\{[A-Za-z_][A-Za-z0-9_]*\\}))*$", + "minLength": 1, + "maxLength": 255 + }, + "name": { + "type": "string", + "description": "Name of this resource.", + "minLength": 1 + }, + "description": { "type": "string", "description": "Description of this resource." }, + "tags": { + "type": "array", + "items": { "type": "string" }, + "description": "Tags to classify this resource." + }, + "mime_type": { "type": "string", "description": "MIME type of this resource." }, + "parameters": { + "type": "array", + "description": "Input parameters for this endpoint.", + "items": { "$ref": "common-types-schema-1.json#/definitions/paramDefinition" } + }, + "return": { + "$ref": "common-types-schema-1.json#/definitions/typeDefinition", + "description": "Description of the output schema." + }, + "language": { + "type": "string", + "default": "sql", + "enum": ["sql", "python"], + "description": "The language used to define the logic of this endpoint. 'sql' or 'python'." + }, + "source": { + "type": "object", + "description": "Source for the endpoint logic, either inline or a file reference.", + "oneOf": [ + { "required": ["code"], "not": { "required": ["file"] } }, + { "required": ["file"], "not": { "required": ["code"] } } + ], + "properties": { + "code": { + "type": "string", + "description": "The inline code snippet to execute." + }, + "file": { + "type": "string", + "description": "A relative path to a file containing the code." + } + }, + "additionalProperties": false + }, + "enabled": { "type": "boolean", "default": true, "description": "Whether this endpoint is active." }, + "tests": { + "type": "array", + "description": "Tests to validate this endpoint.", + "items": { + "type": "object", + "required": ["name", "arguments"], + "properties": { + "name": { "type": "string", "description": "Name of the test." }, + "description": { "type": "string", "description": "What the test checks." }, + "arguments": { + "type": "array", + "items": { + "type": "object", + "required": ["key", "value"], + "properties": { + "key": { "type": "string", "description": "Input parameter to pass to test." }, + "value": { "description": "Value of the input parameter to test." } + }, + "additionalProperties": false + } + }, + "result": { "description": "Expected result." }, + "user_context": { + "type": "object", + "description": "User context for policy testing. Can include role, permissions, user_id, etc." + }, + "result_contains": { + "description": "Partial match - result must contain these fields/values. For arrays, checks if array contains this item." + }, + "result_not_contains": { + "type": "array", + "items": { "type": "string" }, + "description": "List of field names that should NOT be present in the result." + }, + "result_contains_item": { + "description": "For array results - at least one array item must match this object/value." + }, + "result_contains_all": { + "type": "array", + "description": "For array results - all these items must be present (any order)." + }, + "result_length": { + "type": "integer", + "minimum": 0, + "description": "For array results - array must have exactly this many items." + }, + "result_contains_text": { + "type": "string", + "description": "For string results - result must contain this substring." + } + }, + "additionalProperties": false + } + }, + "policies": { + "$ref": "common-types-schema-1.json#/definitions/policySet", + "description": "Policy definitions for access control and data filtering." + } + } + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/assets/schemas/tool-schema-1.json b/skills/mxcp-expert/assets/schemas/tool-schema-1.json new file mode 100644 index 0000000..a03c627 --- /dev/null +++ b/skills/mxcp-expert/assets/schemas/tool-schema-1.json @@ -0,0 +1,168 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "MXCP Tool Definition Schema", + "type": "object", + "required": ["mxcp", "tool"], + "properties": { + "mxcp": { + "type": "integer", + "description": "Schema version. Must be 1.", + "enum": [1], + "default": 1 + }, + + "tool": { + "$ref": "#/definitions/toolDefinition", + "description": "Defines an MCP tool endpoint." + }, + + "metadata": { + "type": "object", + "properties": { + "title": { "type": "string", "description": "Short display title." }, + "description": { "type": "string", "description": "Longer description." } + }, + "description": "Optional metadata for documentation purposes." + } + }, + + "definitions": { + "toolDefinition": { + "type": "object", + "required": ["name", "source"], + "properties": { + "name": { + "type": "string", + "description": "Name of this tool.", + "minLength": 1 + }, + "description": { "type": "string", "description": "Description of this tool." }, + "tags": { + "type": "array", + "items": { "type": "string" }, + "description": "Tags to classify this tool." + }, + "annotations": { + "type": "object", + "description": "Optional behavioral hints for this tool.", + "properties": { + "title": { + "type": "string", + "description": "Human-readable display title for the tool." + }, + "readOnlyHint": { + "type": "boolean", + "description": "Hint: tool does not modify its environment (side-effect-free)." + }, + "destructiveHint": { + "type": "boolean", + "description": "Hint: tool may perform destructive updates (e.g. delete, overwrite)." + }, + "idempotentHint": { + "type": "boolean", + "description": "Hint: repeated calls with same arguments yield the same result." + }, + "openWorldHint": { + "type": "boolean", + "description": "Hint: tool interacts with external systems or entities (non-closed-world)." + } + }, + "additionalProperties": false + }, + "parameters": { + "type": "array", + "description": "Input parameters for this endpoint.", + "items": { "$ref": "common-types-schema-1.json#/definitions/paramDefinition" } + }, + "return": { + "$ref": "common-types-schema-1.json#/definitions/typeDefinition", + "description": "Description of the output schema." + }, + "language": { + "type": "string", + "default": "sql", + "enum": ["sql", "python"], + "description": "The language used to define the logic of this endpoint. 'sql' or 'python'." + }, + "source": { + "type": "object", + "description": "Source for the endpoint logic, either inline or a file reference.", + "oneOf": [ + { "required": ["code"], "not": { "required": ["file"] } }, + { "required": ["file"], "not": { "required": ["code"] } } + ], + "properties": { + "code": { + "type": "string", + "description": "The inline code snippet to execute." + }, + "file": { + "type": "string", + "description": "A relative path to a file containing the code." + } + }, + "additionalProperties": false + }, + "enabled": { "type": "boolean", "default": true, "description": "Whether this endpoint is active." }, + "tests": { + "type": "array", + "description": "Tests to validate this endpoint.", + "items": { + "type": "object", + "required": ["name", "arguments"], + "properties": { + "name": { "type": "string", "description": "Name of the test." }, + "description": { "type": "string", "description": "What the test checks." }, + "arguments": { + "type": "array", + "items": { + "type": "object", + "required": ["key", "value"], + "properties": { + "key": { "type": "string", "description": "Input parameter to pass to test." }, + "value": { "description": "Value of the input parameter to test." } + }, + "additionalProperties": false + } + }, + "result": { "description": "Expected result." }, + "user_context": { + "type": "object", + "description": "User context for policy testing. Can include role, permissions, user_id, etc." + }, + "result_contains": { + "description": "Partial match - result must contain these fields/values. For arrays, checks if array contains this item." + }, + "result_not_contains": { + "type": "array", + "items": { "type": "string" }, + "description": "List of field names that should NOT be present in the result." + }, + "result_contains_item": { + "description": "For array results - at least one array item must match this object/value." + }, + "result_contains_all": { + "type": "array", + "description": "For array results - all these items must be present (any order)." + }, + "result_length": { + "type": "integer", + "minimum": 0, + "description": "For array results - array must have exactly this many items." + }, + "result_contains_text": { + "type": "string", + "description": "For string results - result must contain this substring." + } + }, + "additionalProperties": false + } + }, + "policies": { + "$ref": "common-types-schema-1.json#/definitions/policySet", + "description": "Policy definitions for access control and data filtering." + } + } + } + } +} \ No newline at end of file diff --git a/skills/mxcp-expert/llms.txt b/skills/mxcp-expert/llms.txt new file mode 100644 index 0000000..d4b514b --- /dev/null +++ b/skills/mxcp-expert/llms.txt @@ -0,0 +1,64 @@ +# MXCP documentation +## Readme file +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/README.md + +## Getting started +### Overview +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/getting-started/overview.md + +### Quickstart +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/getting-started/quickstart.md + +## Features +### Overview +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/features/overview.md + +### Auditing +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/features/auditing.md + +### Drift Detection +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/features/drift-detection.md + +### Policies +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/features/policies.md + +### Python endpoints +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/features/python-endpoints.md + +## Guides +### Audit Cleanup +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/audit-cleanup.md + +### Authentication +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/authentication.md + +### Configuration +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/configuration.md + +### Integrations +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/integrations.md + +### Operational +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/operational.md + +### Production Methodology +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/production-methodology.md + +### Quality +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/guides/quality.md + +## Reference +### CLI +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/reference/cli.md + +### Plugins +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/reference/plugins.md + +### Python +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/reference/python.md + +### SQL +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/reference/sql.md + +### Type system +https://raw.githubusercontent.com/raw-labs/mxcp/refs/heads/main/docs/reference/type-system.md \ No newline at end of file diff --git a/skills/mxcp-expert/references/agent-centric-design.md b/skills/mxcp-expert/references/agent-centric-design.md new file mode 100644 index 0000000..640d5db --- /dev/null +++ b/skills/mxcp-expert/references/agent-centric-design.md @@ -0,0 +1,411 @@ +# Agent-Centric Design for MXCP Tools + +**Designing MXCP tools that LLMs can effectively use with zero prior context.** + +## Overview + +When building MXCP servers, remember: **LLMs are your primary users**. Your tools must enable LLMs to accomplish real-world tasks effectively. This guide provides principles for designing tools that work well for AI agents. + +## Core Principles + +### 1. Build for Workflows, Not Just Data Access + +**Don't simply expose database tables or API endpoints - design tools around complete workflows.** + +#### ❌ Poor Design: Raw Data Access +```yaml +# tools/get_user.yml +tool: + name: get_user + description: "Get user by ID" + parameters: + - name: user_id + type: integer + source: + code: SELECT * FROM users WHERE id = $user_id + +# tools/get_orders.yml +tool: + name: get_orders + description: "Get orders by user" + parameters: + - name: user_id + type: integer + source: + code: SELECT * FROM orders WHERE user_id = $user_id +``` + +**Problem**: LLM needs multiple tool calls to answer "What did user 123 buy?" + +#### ✅ Good Design: Workflow-Oriented +```yaml +# tools/get_user_purchase_summary.yml +tool: + name: get_user_purchase_summary + description: "Get complete purchase history for a user including orders, products, and total spending. Use this to understand a user's buying behavior and preferences." + parameters: + - name: user_id + type: integer + description: "User identifier" + - name: date_range + type: string + description: "Optional date range: 'last_30_days', 'last_year', or 'all_time'" + default: "all_time" + return: + type: object + properties: + user_info: { type: object, description: "Basic user information" } + order_count: { type: integer, description: "Total number of orders" } + total_spent: { type: number, description: "Total amount spent in USD" } + top_products: { type: array, description: "Most frequently purchased products" } + source: + code: | + WITH user_orders AS ( + SELECT o.*, p.name as product_name, p.category + FROM orders o + JOIN order_items oi ON o.id = oi.order_id + JOIN products p ON oi.product_id = p.id + WHERE o.user_id = $user_id + AND ($date_range = 'all_time' + OR ($date_range = 'last_30_days' AND o.created_at > CURRENT_DATE - INTERVAL 30 DAY) + OR ($date_range = 'last_year' AND o.created_at > CURRENT_DATE - INTERVAL 1 YEAR)) + ) + SELECT + json_object( + 'user_info', (SELECT json_object('id', id, 'name', name) FROM users WHERE id = $user_id), + 'order_count', COUNT(DISTINCT id), + 'total_spent', SUM(total_amount), + 'top_products', ( + SELECT json_group_array(json_object('product', product_name, 'count', count)) + FROM (SELECT product_name, COUNT(*) as count FROM user_orders GROUP BY product_name ORDER BY count DESC LIMIT 5) + ) + ) as result + FROM user_orders +``` + +**Benefit**: Single tool call answers complete questions about user behavior. + +### 2. Optimize for Limited Context + +**LLMs have constrained context windows - make every token count.** + +#### Design for Concise Responses + +```yaml +tool: + name: search_products + parameters: + - name: query + type: string + description: "Search query" + - name: detail_level + type: string + description: "Response detail level" + enum: ["minimal", "standard", "full"] + default: "standard" + examples: + - "minimal: Only ID, name, price" + - "standard: Basic info + category + stock" + - "full: All fields including descriptions" + source: + code: | + SELECT + CASE $detail_level + WHEN 'minimal' THEN json_object('id', id, 'name', name, 'price', price) + WHEN 'standard' THEN json_object('id', id, 'name', name, 'price', price, 'category', category, 'stock', stock) + ELSE json_object('id', id, 'name', name, 'price', price, 'category', category, 'stock', stock, 'description', description, 'specs', specs) + END as product + FROM products + WHERE name LIKE '%' || $query || '%' +``` + +**Principle**: Default to high-signal information, provide options for more detail. + +#### Use Human-Readable Identifiers + +```yaml +# ✅ GOOD: Return names alongside IDs +return: + type: object + properties: + customer_id: { type: string, description: "Customer ID (e.g., 'CUST_12345')" } + customer_name: { type: string, description: "Customer display name" } + assigned_to_id: { type: string, description: "Assigned user ID" } + assigned_to_name: { type: string, description: "Assigned user name" } + +# ❌ BAD: Only return opaque IDs +return: + type: object + properties: + customer_id: { type: integer } + assigned_to: { type: integer } +``` + +**Benefit**: LLM can understand relationships without additional lookups. + +### 3. Design Actionable Error Messages + +**Error messages should guide LLMs toward correct usage patterns.** + +#### ✅ Good Error Messages (Python Tools) + +```python +def search_large_dataset(query: str, limit: int = 100) -> dict: + """Search with intelligent error guidance""" + + # Validate inputs + if not query or len(query) < 3: + return { + "success": False, + "error": "Query must be at least 3 characters. Provide a more specific search term to get better results.", + "error_code": "QUERY_TOO_SHORT", + "suggestion": "Try adding more keywords or using specific product names" + } + + if limit > 1000: + return { + "success": False, + "error": f"Limit of {limit} exceeds maximum allowed (1000). Use filters to narrow your search: add 'category' or 'price_range' parameters.", + "error_code": "LIMIT_EXCEEDED", + "max_limit": 1000, + "suggestion": "Try using category='electronics' or price_range='0-100' to reduce results" + } + + # Execute search + results = db.execute( + "SELECT * FROM products WHERE name LIKE $query LIMIT $limit", + {"query": f"%{query}%", "limit": limit} + ) + + if not results: + return { + "success": False, + "error": f"No products found matching '{query}'. Try broader terms or check spelling.", + "error_code": "NO_RESULTS", + "suggestion": "Use 'list_categories' tool to see available product categories" + } + + return { + "success": True, + "count": len(results), + "results": results + } +``` + +**Principle**: Every error should suggest a specific next action. + +### 4. Follow Natural Task Subdivisions + +**Tool names should reflect how humans think about tasks, not just database structure.** + +#### ✅ Good: Task-Oriented Naming +``` +get_customer_purchase_history # What users want to know +analyze_sales_by_region # Natural analysis task +check_inventory_status # Action-oriented +schedule_report_generation # Complete workflow +``` + +#### ❌ Poor: Database-Oriented Naming +``` +select_from_orders # Database operation +join_users_and_purchases # Technical operation +aggregate_by_column # Generic operation +``` + +**Use consistent prefixes for discoverability**: +```yaml +# Customer operations +- get_customer_details +- get_customer_orders +- get_customer_analytics + +# Product operations +- search_products +- get_product_details +- check_product_availability + +# Analytics operations +- analyze_sales_trends +- analyze_customer_behavior +- analyze_inventory_turnover +``` + +### 5. Provide Comprehensive Documentation + +**Every field must have a description that helps LLMs understand usage.** + +See **references/llm-friendly-documentation.md** for complete documentation guidelines. + +**Quick checklist**: +- [ ] Tool description explains WHAT, returns WHAT, WHEN to use +- [ ] Every parameter has description with examples +- [ ] Return type properties all have descriptions +- [ ] Cross-references to related tools +- [ ] Examples show realistic usage + +## MXCP-Specific Best Practices + +### Use SQL for Workflow Consolidation + +**SQL is powerful for combining multiple data sources in one query:** + +```yaml +tool: + name: get_order_fulfillment_status + description: "Get complete order fulfillment information including shipping, payments, and inventory status. Use this to answer questions about order status and estimated delivery." + source: + code: | + SELECT + o.id as order_id, + o.status as order_status, + u.name as customer_name, + s.carrier, + s.tracking_number, + s.estimated_delivery, + p.status as payment_status, + json_group_array( + json_object( + 'product', prod.name, + 'quantity', oi.quantity, + 'in_stock', prod.stock >= oi.quantity + ) + ) as items + FROM orders o + JOIN users u ON o.user_id = u.id + LEFT JOIN shipments s ON o.id = s.order_id + LEFT JOIN payments p ON o.id = p.order_id + JOIN order_items oi ON o.id = oi.order_id + JOIN products prod ON oi.product_id = prod.id + WHERE o.id = $order_id + GROUP BY o.id +``` + +**Single tool call provides complete fulfillment picture.** + +### Use Python for Complex Workflows + +```python +async def analyze_customer_churn_risk(customer_id: str) -> dict: + """ + Comprehensive churn risk analysis combining multiple data sources. + + Returns risk score, contributing factors, and recommended actions. + Use this to identify customers who may leave and take preventive action. + """ + # Get customer history + orders = db.execute( + "SELECT * FROM orders WHERE customer_id = $cid ORDER BY created_at DESC", + {"cid": customer_id} + ) + + support_tickets = db.execute( + "SELECT * FROM support_tickets WHERE customer_id = $cid", + {"cid": customer_id} + ) + + # Calculate risk factors + days_since_last_order = (datetime.now() - orders[0]["created_at"]).days if orders else 999 + unresolved_tickets = len([t for t in support_tickets if t["status"] != "resolved"]) + total_spent = sum(o["total_amount"] for o in orders) + + # Determine risk level + risk_score = 0 + factors = [] + + if days_since_last_order > 90: + risk_score += 30 + factors.append("No purchases in 90+ days") + + if unresolved_tickets > 0: + risk_score += 20 * unresolved_tickets + factors.append(f"{unresolved_tickets} unresolved support tickets") + + if total_spent < 100: + risk_score += 10 + factors.append("Low lifetime value") + + # Generate recommendations + recommendations = [] + if days_since_last_order > 90: + recommendations.append("Send re-engagement email with discount") + if unresolved_tickets > 0: + recommendations.append("Prioritize resolution of open support tickets") + + return { + "success": True, + "customer_id": customer_id, + "risk_score": min(risk_score, 100), + "risk_level": "high" if risk_score > 60 else "medium" if risk_score > 30 else "low", + "contributing_factors": factors, + "recommendations": recommendations, + "days_since_last_order": days_since_last_order, + "unresolved_tickets": unresolved_tickets + } +``` + +### Leverage MXCP Policies for Context-Aware Tools + +```yaml +tool: + name: get_employee_compensation + description: "Get employee compensation details. Returns salary and benefits information based on user permissions." + parameters: + - name: employee_id + type: string + description: "Employee identifier" + return: + type: object + properties: + employee_id: { type: string } + name: { type: string } + salary: { type: number, description: "Annual salary (admin only)" } + benefits: { type: array, description: "Benefits package" } + policies: + output: + - condition: "user.role != 'hr_manager' && user.role != 'admin'" + action: filter_fields + fields: ["salary"] + reason: "Salary information restricted to HR managers and admins" + source: + code: | + SELECT + employee_id, + name, + salary, + benefits + FROM employees + WHERE employee_id = $employee_id +``` + +**LLM can call same tool, MXCP automatically filters based on user context.** + +## Testing Agent-Centric Design + +### Create Realistic Evaluation Scenarios + +See **references/mxcp-evaluation-guide.md** for complete evaluation guidelines. + +**Quick validation**: +1. Can an LLM answer complex multi-step questions using your tools? +2. Do tool descriptions clearly indicate when to use each tool? +3. Do error messages guide the LLM toward correct usage? +4. Can common tasks be completed with minimal tool calls? + +## Summary + +**Agent-centric design principles for MXCP**: + +1. ✅ **Build for workflows** - Consolidate related operations +2. ✅ **Optimize for context** - Provide detail level options, use readable identifiers +3. ✅ **Actionable errors** - Guide LLMs with specific suggestions +4. ✅ **Natural naming** - Task-oriented, not database-oriented +5. ✅ **Comprehensive docs** - Every parameter and field documented + +**MXCP advantages**: +- SQL enables powerful workflow consolidation +- Python handles complex multi-step logic +- Policies provide automatic context-aware filtering +- Type system ensures clear contracts + +**Remember**: Design for the LLM as your user, not the human. Humans configure tools, LLMs use them. diff --git a/skills/mxcp-expert/references/build-and-validate-workflow.md b/skills/mxcp-expert/references/build-and-validate-workflow.md new file mode 100644 index 0000000..037a11f --- /dev/null +++ b/skills/mxcp-expert/references/build-and-validate-workflow.md @@ -0,0 +1,990 @@ +# Build and Validate Workflow + +**Mandatory workflow to ensure MXCP servers always work correctly.** + +## Definition of Done + +An MXCP server is **DONE** only when ALL of these criteria are met: + +- [ ] **Virtual environment created**: `uv venv` completed (if Python tools exist) +- [ ] **Dependencies installed**: `uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov` (if Python tools exist) +- [ ] **Structure valid**: `mxcp validate` passes with no errors +- [ ] **MXCP tests pass**: `mxcp test` passes for all tools +- [ ] **Python code formatted**: `black python/` passes (if Python tools exist) +- [ ] **Type checking passes**: `pyright python/` passes with 0 errors (if Python tools exist) +- [ ] **Python unit tests pass**: `pytest tests/ -v` passes (if Python tools exist) +- [ ] **Data quality**: `dbt test` passes (if using dbt) +- [ ] **Result correctness verified**: Tests check actual values, not just structure +- [ ] **Mocking implemented**: External API calls are mocked in unit tests +- [ ] **Concurrency safe**: Python tools avoid race conditions +- [ ] **Documentation quality verified**: LLMs can understand tools with zero context +- [ ] **Error handling implemented**: Python tools return structured errors +- [ ] **Manual verification**: At least one manual test per tool succeeds +- [ ] **Security reviewed**: Checklist completed (see below) +- [ ] **Config provided**: Project has config.yml with usage instructions +- [ ] **Dependencies listed**: requirements.txt includes all dev dependencies + +**NEVER declare a project complete without ALL checkboxes checked.** + +## Mandatory Build Order + +Follow this exact order to ensure correctness: + +### Phase 1: Foundation (Must complete before Phase 2) + +1. **Initialize project** + ```bash + mkdir project-name && cd project-name + mxcp init --bootstrap + ``` + +2. **Set up Python virtual environment** (CRITICAL - do this BEFORE any MXCP commands) + ```bash + # Create virtual environment with uv + uv venv + + # Activate virtual environment + source .venv/bin/activate # On Unix/macOS + # OR + .venv\Scripts\activate # On Windows + + # Verify activation (prompt should show (.venv)) + which python + # Output: /path/to/project-name/.venv/bin/python + + # Install MXCP and development tools + uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov + + # Create requirements.txt for reproducibility + cat > requirements.txt <<'EOF' +mxcp>=0.1.0 +black>=24.0.0 +pyright>=1.1.0 +pytest>=7.0.0 +pytest-asyncio>=0.21.0 +pytest-httpx>=0.21.0 +pytest-cov>=4.0.0 +EOF + ``` + + **IMPORTANT**: Virtual environment must be active for ALL subsequent commands. If you close your terminal, re-activate with `source .venv/bin/activate`. + +3. **Create project structure** + ```bash + mkdir -p seeds models tools resources prompts python tests + touch tests/__init__.py + ``` + +4. **Set up dbt (if needed)** + ```bash + # Create dbt_project.yml if needed + # Create profiles.yml connection + ``` + +5. **Validation checkpoint**: Verify structure + ```bash + # Ensure virtual environment is active + echo $VIRTUAL_ENV # Should show: /path/to/project-name/.venv + + ls -la # Confirm directories exist + mxcp validate # Should pass (no tools yet, but structure valid) + ``` + +**CRITICAL: Directory Structure Enforcement** + +MXCP **enforces** organized directory structure. Files in wrong directories are **ignored** by discovery commands: + +- ✅ Tools MUST be in `tools/*.yml` +- ✅ Resources MUST be in `resources/*.yml` +- ✅ Prompts MUST be in `prompts/*.yml` +- ❌ Tool files in root directory will be **ignored** +- ❌ Tool files in wrong directories will be **ignored** + +**Common mistake to avoid**: +```bash +# ❌ WRONG - tool in root directory (will be ignored) +my_tool.yml + +# ✅ CORRECT - tool in tools/ directory +tools/my_tool.yml +``` + +Use `mxcp init --bootstrap` to create proper structure automatically. + +### Phase 2: Data Layer (if applicable) + +1. **Add data source** (CSV, Excel, etc.) + ```bash + # Option A: CSV seed + cp data.csv seeds/ + + # Option B: Excel conversion + python -c "import pandas as pd; pd.read_excel('data.xlsx').to_csv('seeds/data.csv', index=False)" + ``` + +2. **Create schema.yml** (CRITICAL - don't skip!) + ```yaml + # seeds/schema.yml + version: 2 + seeds: + - name: data + description: "Data description here" + columns: + - name: id + tests: [unique, not_null] + # Add ALL columns with tests + ``` + +3. **Load and test data** + ```bash + dbt seed --select data + dbt test --select data + ``` + +4. **Validation checkpoint**: Data quality verified + ```bash + # Check data loaded + mxcp query "SELECT COUNT(*) FROM data" + # Should return row count + ``` + +### Phase 3: Build Tools ONE AT A TIME + +**CRITICAL: Build ONE tool, validate, test, THEN move to next.** + +For EACH tool: + +#### Step 1: Create Test FIRST (with LLM-friendly documentation) + +```yaml +# tools/my_tool.yml +mxcp: 1 +tool: + name: my_tool + description: "Retrieve data from table by filtering on column. Returns array of matching records. Use this to query specific records by their identifier." + parameters: + - name: param1 + type: string + description: "Filter value for column (e.g., 'value123'). Must match exact column value." + required: true + examples: ["value123", "test_value"] + return: + type: array + description: "Array of matching records" + items: + type: object + properties: + id: { type: integer, description: "Record identifier" } + column: { type: string, description: "Filtered column value" } + source: + code: | + SELECT * FROM data WHERE column = $param1 + tests: + - name: "basic_test" + arguments: + - key: param1 + value: "test_value" + result: + # Expected result structure with actual values to verify + - id: 1 + column: "test_value" +``` + +**Documentation requirements (check before proceeding)**: +- [ ] Tool description explains WHAT, returns WHAT, WHEN to use +- [ ] Parameters have descriptions with examples +- [ ] Return type properties all described +- [ ] An LLM with zero context could understand how to use this + +#### Step 2: Validate Structure + +```bash +mxcp validate +# Must pass before proceeding +``` + +**Common errors at this stage:** +- Indentation wrong (use spaces, not tabs) +- Missing required fields (name, description, return) +- Type mismatch (array vs object) +- Invalid SQL syntax + +**If validation fails:** +1. Read error message carefully +2. Check YAML indentation (use yamllint) +3. Verify all required fields present +4. Check type definitions match return data +5. Fix and re-validate + +#### Step 3: Test Functionality + +**A. MXCP Integration Tests** + +```bash +# Run the test case +mxcp test tool my_tool + +# Run manually with different inputs +mxcp run tool my_tool --param param1=test_value +``` + +**If test fails:** +1. Check SQL syntax in source +2. Verify table/column names exist +3. Test SQL directly: `mxcp query "SELECT ..."` +4. Check parameter binding ($param1 syntax) +5. Verify return type matches actual data +6. Fix and re-test + +**B. Python Code Quality (For Python Tools)** + +**MANDATORY workflow after creating or editing ANY Python file:** + +```bash +# CRITICAL: Always ensure virtual environment is active first +source .venv/bin/activate + +# Step 1: Format code with black +black python/ +# Must see: "All done! ✨ 🍰 ✨" or "N file(s) reformatted" + +# Step 2: Type check with pyright +pyright python/ +# Must see: "0 errors, 0 warnings, 0 informations" + +# Step 3: Run unit tests +pytest tests/ -v +# Must see: All tests PASSED + +# If ANY step fails, fix before proceeding! +``` + +**Create Unit Tests:** + +```bash +# Create test file +cat > tests/test_my_tool.py <<'EOF' +"""Tests for my_module.""" + +import pytest +from python.my_module import my_function +from typing import Dict, Any + +def test_my_function_correctness(): + """Verify result correctness""" + result = my_function("test_input") + assert result["expected_key"] == "expected_value" # Verify actual value! + +@pytest.mark.asyncio +async def test_async_function(): + """Test async functions""" + result = await async_function() + assert result is not None +EOF + +# Run tests with coverage +pytest tests/ -v --cov=python --cov-report=term-missing +``` + +**Common Python Type Errors and Fixes:** + +```python +# ❌ WRONG: Using 'any' type +from typing import Dict +async def get_data(id: str) -> Dict[str, any]: # 'any' is not valid + pass + +# ✅ CORRECT: Use proper types +from typing import Dict, Any, Union +async def get_data(id: str) -> Dict[str, Union[str, int, float, bool]]: + pass + +# ✅ BETTER: Define response type +from typing import TypedDict +class DataResponse(TypedDict): + success: bool + data: str + count: int + +async def get_data(id: str) -> DataResponse: + pass +``` + +**If unit tests fail:** +1. Check function logic +2. Verify test assertions are correct +3. Check imports +4. Fix and re-test + +**C. Mocking External Calls (Required for API tools)** + +```python +# tests/test_api_tool.py +import pytest +from python.api_wrapper import fetch_data + +@pytest.mark.asyncio +async def test_fetch_data_with_mock(httpx_mock): + """Mock external API call""" + # Mock the HTTP response + httpx_mock.add_response( + url="https://api.example.com/data", + json={"key": "value", "count": 5} + ) + + # Call function + result = await fetch_data("param") + + # Verify correctness + assert result["key"] == "value" + assert result["count"] == 5 +``` + +**D. Error Handling (Required for Python tools)** + +Python tools MUST return structured error objects, never raise exceptions to MXCP. + +```python +# python/my_module.py +import httpx + +async def fetch_user(user_id: int) -> dict: + """ + Fetch user with comprehensive error handling. + + Returns: + Success: {"success": True, "user": {...}} + Error: {"success": False, "error": "...", "error_code": "..."} + """ + try: + async with httpx.AsyncClient(timeout=10.0) as client: + response = await client.get( + f"https://api.example.com/users/{user_id}" + ) + + if response.status_code == 404: + return { + "success": False, + "error": f"User with ID {user_id} not found. Use list_users to see available users.", + "error_code": "NOT_FOUND", + "user_id": user_id + } + + if response.status_code >= 500: + return { + "success": False, + "error": "External API is currently unavailable. Please try again later.", + "error_code": "API_ERROR", + "status_code": response.status_code + } + + response.raise_for_status() + + return { + "success": True, + "user": response.json() + } + + except httpx.TimeoutException: + return { + "success": False, + "error": "Request timed out after 10 seconds. The API may be slow or unavailable.", + "error_code": "TIMEOUT" + } + + except Exception as e: + return { + "success": False, + "error": f"Unexpected error: {str(e)}", + "error_code": "UNKNOWN_ERROR" + } +``` + +**Test error handling**: +```python +# tests/test_error_handling.py +@pytest.mark.asyncio +async def test_user_not_found(httpx_mock): + """Verify 404 returns structured error""" + httpx_mock.add_response( + url="https://api.example.com/users/999", + status_code=404 + ) + + result = await fetch_user(999) + + assert result["success"] is False + assert result["error_code"] == "NOT_FOUND" + assert "999" in result["error"] # Error mentions the ID + assert "list_users" in result["error"] # Actionable suggestion + +@pytest.mark.asyncio +async def test_timeout_error(httpx_mock): + """Verify timeout returns structured error""" + httpx_mock.add_exception(httpx.TimeoutException("Timeout")) + + result = await fetch_user(123) + + assert result["success"] is False + assert result["error_code"] == "TIMEOUT" + assert "timeout" in result["error"].lower() +``` + +**Error message principles**: +- ✅ Be specific (exactly what went wrong) +- ✅ Be actionable (suggest next steps) +- ✅ Provide context (relevant values/IDs) +- ✅ Use plain language (LLM-friendly) + +See **references/error-handling-guide.md** for comprehensive patterns. + +**E. Concurrency Safety Tests (For stateful Python tools)** + +```python +# tests/test_concurrency.py +import pytest +import asyncio + +@pytest.mark.asyncio +async def test_concurrent_calls(): + """Verify no race conditions""" + tasks = [my_function(i) for i in range(100)] + results = await asyncio.gather(*tasks) + + # Verify all succeeded + assert len(results) == 100 + assert all(r is not None for r in results) +``` + +#### Step 4: Verification Checkpoint + +Before moving to next tool: + +**For ALL tools:** +- [ ] `mxcp validate` passes +- [ ] `mxcp test tool my_tool` passes +- [ ] Manual test with real data works +- [ ] Tool returns expected data structure +- [ ] Error cases handled (null params, no results, etc.) +- [ ] **Result correctness verified** (not just structure) +- [ ] **Documentation quality verified**: + - [ ] Tool description explains WHAT, WHAT it returns, WHEN to use + - [ ] All parameters have descriptions with examples + - [ ] Return fields all have descriptions + - [ ] Cross-references to related tools (if applicable) +- [ ] **LLM can understand with zero context** (test: read YAML only, would you know how to use it?) + +**For Python tools (additionally):** +- [ ] **Virtual environment active**: `echo $VIRTUAL_ENV` shows path +- [ ] **Code formatted**: `black python/` shows "All done!" +- [ ] **Type checking passes**: `pyright python/` shows "0 errors" +- [ ] `pytest tests/test_my_tool.py -v` passes +- [ ] External calls are mocked (if applicable) +- [ ] Concurrency tests pass (if stateful) +- [ ] No global mutable state OR proper locking used +- [ ] Test coverage >80% (`pytest --cov=python tests/`) +- [ ] **Error handling implemented**: + - [ ] All try/except blocks return structured errors + - [ ] Error format: `{"success": False, "error": "...", "error_code": "..."}` + - [ ] Error messages are specific and actionable + - [ ] Never raise exceptions to MXCP (return error objects) + +**Only proceed to next tool when ALL checks pass.** + +### Phase 4: Integration Testing + +After all tools created: + +1. **Run full validation suite** + ```bash + # CRITICAL: Ensure virtual environment is active + source .venv/bin/activate + + # Python code quality (if Python tools exist) + black python/ # Must show: "All done!" + pyright python/ # Must show: "0 errors" + pytest tests/ -v --cov=python --cov-report=term # All tests must pass + + # MXCP validation and integration tests + mxcp validate # All tools + mxcp test # All tests + mxcp lint # Documentation quality + + # dbt tests (if applicable) + dbt test + ``` + +2. **Test realistic scenarios** + ```bash + # Test each tool with realistic inputs + mxcp run tool tool1 --param key=realistic_value + mxcp run tool tool2 --param key=realistic_value + + # Test error cases + mxcp run tool tool1 --param key=invalid_value + mxcp run tool tool1 # Missing required param + ``` + +3. **Performance check** (if applicable) + ```bash + # Test with large inputs + mxcp run tool query_data --param limit=1000 + + # Check response time is reasonable + time mxcp run tool my_tool --param key=value + ``` + +### Phase 5: Security & Configuration + +1. **Security review checklist** + - [ ] All SQL uses parameterized queries ($param) + - [ ] No hardcoded secrets in code + - [ ] Input validation on all parameters + - [ ] Sensitive fields filtered with policies (if needed) + - [ ] Authentication configured (if needed) + +2. **Create config.yml** + ```yaml + # config.yml + mxcp: 1 + profiles: + default: + secrets: + - name: secret_name + type: env + parameters: + env_var: SECRET_ENV_VAR + ``` + +3. **Create README or usage instructions** + ```markdown + # Project Name + + ## Setup + 1. Install dependencies: pip install -r requirements.txt + 2. Set environment variables: export SECRET=xxx + 3. Load data: dbt seed (if applicable) + 4. Start server: mxcp serve + + ## Available Tools + - tool1: Description + - tool2: Description + ``` + +### Phase 6: Final Validation + +**This is the FINAL checklist before declaring DONE:** + +```bash +# 0. Activate virtual environment +source .venv/bin/activate +echo $VIRTUAL_ENV # Must show path + +# 1. Python code quality (if Python tools exist) +black python/ && pyright python/ && pytest tests/ -v +# All must pass + +# 2. Clean start test +cd .. && cd project-name +mxcp validate +# Should pass + +# 3. All tests pass +mxcp test +# Should show all tests passing + +# 4. Manual smoke test +mxcp run tool --param key=value +# Should return valid data + +# 5. Lint check +mxcp lint +# Should have no critical issues + +# 6. dbt tests (if applicable) +dbt test +# All data quality tests pass + +# 7. Serve test +mxcp serve --transport http --port 8080 & +SERVER_PID=$! +sleep 2 +curl http://localhost:8080/health || true +kill $SERVER_PID +# Server should start without errors +``` + +## Common Failure Patterns & Fixes + +### YAML Validation Errors + +**Error**: "Invalid YAML: expected " +```yaml +# WRONG: Mixed spaces and tabs +tool: + name: my_tool + description: "..." # Tab here + +# CORRECT: Consistent spaces (2 or 4) +tool: + name: my_tool + description: "..." +``` + +**Error**: "Missing required field: description" +```yaml +# WRONG: Missing description +tool: + name: my_tool + parameters: [...] + +# CORRECT: All required fields +tool: + name: my_tool + description: "What this tool does" + parameters: [...] +``` + +**Error**: "Invalid type for field 'return'" +```yaml +# WRONG: String instead of type object +return: "array" + +# CORRECT: Proper type definition +return: + type: array + items: + type: object +``` + +### SQL Errors + +**Error**: "Table 'xyz' not found" +```sql +-- WRONG: Table doesn't exist +SELECT * FROM xyz + +-- FIX: Check table name, run dbt seed +SELECT * FROM actual_table_name + +-- VERIFY: List tables +-- mxcp query "SHOW TABLES" +``` + +**Error**: "Column 'abc' not found" +```sql +-- WRONG: Column name typo or doesn't exist +SELECT abc FROM table + +-- FIX: Check exact column name (case-sensitive in some DBs) +SELECT actual_column_name FROM table + +-- VERIFY: List columns +-- mxcp query "DESCRIBE table" +``` + +**Error**: "Unbound parameter: $param1" +```yaml +# WRONG: Parameter not defined in parameters list +parameters: + - name: other_param +source: + code: SELECT * FROM table WHERE col = $param1 + +# CORRECT: Define all parameters used in SQL +parameters: + - name: param1 + type: string +source: + code: SELECT * FROM table WHERE col = $param1 +``` + +### Type Mismatch Errors + +**Error**: "Expected object, got array" +```yaml +# WRONG: Return type doesn't match actual data +return: + type: object +source: + code: SELECT * FROM table # Returns multiple rows (array) + +# CORRECT: Match return type to SQL result +return: + type: array + items: + type: object +source: + code: SELECT * FROM table +``` + +**Error**: "Expected string, got number" +```yaml +# WRONG: Parameter type doesn't match usage +parameters: + - name: age + type: string +source: + code: SELECT * FROM users WHERE age > $age # Numeric comparison + +# CORRECT: Use appropriate type +parameters: + - name: age + type: integer +source: + code: SELECT * FROM users WHERE age > $age +``` + +### Python Import Errors + +**Error**: "ModuleNotFoundError: No module named 'pandas'" +```bash +# WRONG: Library not installed OR virtual environment not active +import pandas as pd + +# FIX: +# 1. Ensure virtual environment is active +source .venv/bin/activate + +# 2. Add to requirements.txt +echo "pandas>=2.0.0" >> requirements.txt + +# 3. Install using uv +uv pip install pandas +``` + +**Error**: "ImportError: cannot import name 'db' from 'mxcp.runtime'" +```python +# WRONG: Import path incorrect +from mxcp import db + +# CORRECT: Import from runtime +from mxcp.runtime import db +``` + +### Python Code Quality Errors + +**Error**: Black formatting fails with "INTERNAL ERROR" +```bash +# WRONG: Syntax error in Python code +# FIX: Check syntax first +python -m py_compile python/your_file.py +# Fix syntax errors, then run black +black python/ +``` + +**Error**: Pyright shows "Type of 'any' is unknown" +```python +# WRONG: Using lowercase 'any' +def get_data() -> Dict[str, any]: + pass + +# CORRECT: Use 'Any' from typing +from typing import Dict, Any +def get_data() -> Dict[str, Any]: + pass +``` + +**Error**: "command not found: mxcp" +```bash +# WRONG: Virtual environment not active +mxcp validate + +# FIX: Activate virtual environment +source .venv/bin/activate +which mxcp # Should show: /path/to/project/.venv/bin/mxcp +mxcp validate +``` + +### dbt Errors + +**Error**: "Seed file not found" +```bash +# WRONG: File not in seeds/ directory +dbt seed --select data + +# FIX: Check file location +ls seeds/ +# Ensure data.csv exists in seeds/ + +# Or check seed name matches filename +# seeds/my_data.csv → dbt seed --select my_data +``` + +**Error**: "Test failed: unique_column_id" +```yaml +# Data has duplicates +# FIX: Clean data or remove test +seeds: + - name: data + columns: + - name: id + tests: [unique] # Remove if duplicates are valid +``` + +## Debugging Workflow + +When something doesn't work: + +### Step 1: Identify the Layer + +- **YAML layer**: `mxcp validate` fails → YAML structure issue +- **SQL layer**: `mxcp test` fails but validate passes → SQL issue +- **Data layer**: SQL syntax OK but wrong results → Data issue +- **Type layer**: Runtime error about types → Type mismatch +- **Python layer**: Import or runtime error → Python code issue + +### Step 2: Isolate the Problem + +```bash +# Test YAML structure +mxcp validate --debug + +# Test SQL directly +mxcp query "SELECT * FROM table LIMIT 5" + +# Test tool with minimal input +mxcp run tool my_tool --param key=simple_value + +# Check logs +mxcp serve --debug +# Look for error messages +``` + +### Step 3: Fix Incrementally + +1. **Fix one error at a time** +2. **Re-validate after each fix** +3. **Don't move forward until green** + +### Step 4: Verify Fix + +```bash +# After fixing, run full suite +mxcp validate && mxcp test && mxcp lint + +# If all pass, manual test +mxcp run tool my_tool --param key=test_value +``` + +## Self-Checking for Agents + +**Before declaring a project complete, agent must verify:** + +### 0. Is virtual environment set up? (CRITICAL) +```bash +# Check virtual environment exists +ls .venv/bin/activate # Must exist + +# Activate it +source .venv/bin/activate + +# Verify activation +echo $VIRTUAL_ENV # Must show: /path/to/project/.venv +which python # Must show: /path/to/project/.venv/bin/python +``` + +### 1. Can project be initialized? +```bash +cd project-directory +ls mxcp-site.yml # Must exist +``` + +### 2. Python code quality passes? (if Python tools exist) +```bash +# Ensure venv active first +source .venv/bin/activate + +# Check formatting +black --check python/ +# Exit code 0 = success + +# Check types +pyright python/ +# Must show: "0 errors, 0 warnings, 0 informations" + +# Check tests +pytest tests/ -v +# All tests show PASSED +``` + +### 3. Does MXCP validation pass? +```bash +# Ensure venv active +source .venv/bin/activate + +mxcp validate +# Exit code 0 = success +``` + +### 4. Do MXCP tests pass? +```bash +# Ensure venv active +source .venv/bin/activate + +mxcp test +# All tests show PASSED +``` + +### 5. Can tools be executed? +```bash +# Ensure venv active +source .venv/bin/activate + +mxcp run tool --param key=value +# Returns data without errors +``` + +### 6. Is configuration complete? +```bash +ls config.yml # Exists +cat config.yml | grep "mxcp: 1" # Valid +``` + +### 7. Are dependencies listed? +```bash +# Must have requirements.txt with all dependencies +ls requirements.txt # Exists +cat requirements.txt # Has mxcp, black, pyright, pytest +``` + +### 8. Can server start? +```bash +# Ensure venv active +source .venv/bin/activate + +timeout 5 mxcp serve --transport http --port 8080 || true +# Should start without immediate errors +``` + +## Retry Strategy + +If validation fails: + +### Attempt 1: Fix Based on Error Message +- Read error message carefully +- Apply specific fix +- Re-validate + +### Attempt 2: Check Examples +- Compare with working examples +- Verify structure matches pattern +- Re-validate + +### Attempt 3: Simplify +- Remove optional features +- Test minimal version +- Add features back incrementally + +### If Still Failing: +- Report exact error to user +- Provide working minimal example +- Ask for clarification on requirements + +## Summary: The Golden Rule + +**Build → Validate → Test → Verify → THEN Next** + +Never skip steps. Never batch multiple tools without validating each one. Always verify before declaring done. + +**If validation fails, the project is NOT done. Fix until all checks pass.** diff --git a/skills/mxcp-expert/references/claude-desktop.md b/skills/mxcp-expert/references/claude-desktop.md new file mode 100644 index 0000000..45ce65a --- /dev/null +++ b/skills/mxcp-expert/references/claude-desktop.md @@ -0,0 +1,264 @@ +# Claude Desktop Integration + +Guide to connecting MXCP servers with Claude Desktop. + +## Quick Setup + +### 1. Initialize MXCP Project + +```bash +mkdir my-mxcp-tools && cd my-mxcp-tools +mxcp init --bootstrap +``` + +The `--bootstrap` flag automatically creates `server_config.json` with the correct configuration for your environment. + +### 2. Locate Claude Config + +**macOS**: +``` +~/Library/Application Support/Claude/claude_desktop_config.json +``` + +**Windows**: +``` +%APPDATA%\Claude\claude_desktop_config.json +``` + +**Linux**: +``` +~/.config/Claude/claude_desktop_config.json +``` + +### 3. Add MXCP Server + +Edit the Claude config file: + +```json +{ + "mcpServers": { + "my-tools": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/absolute/path/to/my-mxcp-tools" + } + } +} +``` + +**Important**: Use absolute paths for `cwd`. + +### 4. Restart Claude Desktop + +Close and reopen Claude Desktop. Your tools should now be available. + +## Verifying Connection + +### Check Tool Availability + +Ask Claude: +- "What tools do you have available?" +- "List the MXCP tools you can use" + +### Test a Tool + +Ask Claude to use one of your tools: +- "Use the hello_world tool with name='Claude'" +- "Show me what the calculate_fibonacci tool does" + +## Environment-Specific Configurations + +### Virtual Environment + +```json +{ + "mcpServers": { + "my-tools": { + "command": "/path/to/venv/bin/mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/path/to/project" + } + } +} +``` + +### Poetry Project + +```json +{ + "mcpServers": { + "my-tools": { + "command": "poetry", + "args": ["run", "mxcp", "serve", "--transport", "stdio"], + "cwd": "/path/to/project" + } + } +} +``` + +### System-Wide Installation + +```json +{ + "mcpServers": { + "my-tools": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/path/to/project" + } + } +} +``` + +## Multiple MCP Servers + +You can connect multiple MXCP projects: + +```json +{ + "mcpServers": { + "company-data": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/path/to/company-data-project" + }, + "ml-tools": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/path/to/ml-tools-project" + }, + "external-apis": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio"], + "cwd": "/path/to/external-apis-project" + } + } +} +``` + +## Using Profiles + +Connect to different environments: + +```json +{ + "mcpServers": { + "company-dev": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio", "--profile", "dev"], + "cwd": "/path/to/project" + }, + "company-prod": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio", "--profile", "prod"], + "cwd": "/path/to/project" + } + } +} +``` + +## Troubleshooting + +### Tools Not Appearing + +1. Check Claude config syntax: + ```bash + cat ~/Library/Application\ Support/Claude/claude_desktop_config.json | jq + ``` + +2. Verify MXCP installation: + ```bash + which mxcp + mxcp --version + ``` + +3. Test server manually: + ```bash + cd /path/to/project + mxcp serve --transport stdio + # Should wait for input + # Press Ctrl+C to exit + ``` + +4. Check project structure: + ```bash + ls -la /path/to/project + # Should see mxcp-site.yml and tools/ directory + ``` + +### Connection Errors + +**Error: Command not found** +- Use absolute path to mxcp executable +- Check virtual environment activation + +**Error: Permission denied** +- Ensure mxcp executable has execute permissions +- Check directory permissions + +**Error: No tools available** +- Verify `tools/` directory exists +- Run `mxcp validate` to check endpoints +- Check `mxcp-site.yml` configuration + +### Debug Mode + +Enable debug logging: + +```json +{ + "mcpServers": { + "my-tools": { + "command": "mxcp", + "args": ["serve", "--transport", "stdio", "--debug"], + "cwd": "/path/to/project" + } + } +} +``` + +Check Claude logs: +- macOS: `~/Library/Logs/Claude/` +- Windows: `%APPDATA%\Claude\logs\` +- Linux: `~/.config/Claude/logs/` + +## Best Practices + +1. **Use --bootstrap** - Creates correct config automatically +2. **Absolute Paths** - Always use absolute paths in config +3. **Test Locally** - Run `mxcp serve` manually before adding to Claude +4. **Multiple Projects** - Organize related tools in separate projects +5. **Profiles** - Use different profiles for dev/staging/prod +6. **Validation** - Run `mxcp validate` before deployment +7. **Version Control** - Keep `server_config.json` in .gitignore + +## Example Workflow + +```bash +# 1. Create project +mkdir my-tools && cd my-tools +mxcp init --bootstrap + +# 2. Test locally +mxcp serve +# Ctrl+C to exit + +# 3. Copy config path from server_config.json +cat server_config.json + +# 4. Edit Claude config +vim ~/Library/Application\ Support/Claude/claude_desktop_config.json + +# 5. Restart Claude Desktop + +# 6. Test in Claude +# Ask: "What tools do you have?" +``` + +## Security Notes + +- Never commit API keys in Claude config +- Use secrets management (Vault, 1Password) +- Set appropriate file permissions +- Use read-only mode for production: `--readonly` +- Enable audit logging for compliance diff --git a/skills/mxcp-expert/references/cli-reference.md b/skills/mxcp-expert/references/cli-reference.md new file mode 100644 index 0000000..de443b0 --- /dev/null +++ b/skills/mxcp-expert/references/cli-reference.md @@ -0,0 +1,432 @@ +# CLI Reference + +Quick reference for MXCP command-line interface. + +## Core Commands + +### mxcp init + +Initialize new MXCP project. + +```bash +mxcp init # Current directory +mxcp init my-project # New directory +mxcp init --bootstrap # With examples +mxcp init --project=myapp --profile=dev +``` + +### mxcp serve + +Start MCP server. + +```bash +mxcp serve # Use config defaults +mxcp serve --transport stdio # For Claude Desktop +mxcp serve --transport http --port 8080 +mxcp serve --profile production +mxcp serve --sql-tools true # Enable SQL query tools +mxcp serve --readonly # Read-only database +mxcp serve --stateless # For serverless deployment +mxcp serve --debug # Debug mode +``` + +### mxcp list + +List available endpoints. + +```bash +mxcp list # All endpoints +mxcp list --json-output # JSON format +mxcp list --profile prod # Specific profile +``` + +### mxcp run + +Execute endpoint. + +```bash +# Tools +mxcp run tool my_tool --param name=value + +# Resources +mxcp run resource my_resource --param id=123 + +# Prompts +mxcp run prompt my_prompt --param text="hello" + +# Complex parameters from JSON file +mxcp run tool analyze --param data=@input.json + +# With user context +mxcp run tool secure_data --user-context '{"role": "admin"}' + +# Read-only mode +mxcp run tool query_data --readonly +``` + +## Quality Commands + +### mxcp validate + +Check structure and types. + +```bash +mxcp validate # All endpoints +mxcp validate my_tool # Specific endpoint +mxcp validate --json-output # JSON format +mxcp validate --readonly # Read-only database +``` + +### mxcp test + +Run endpoint tests. + +```bash +mxcp test # All tests +mxcp test tool my_tool # Specific endpoint +mxcp test --user-context '{"role": "admin"}' +mxcp test --user-context @user.json +mxcp test --json-output +mxcp test --readonly +``` + +### mxcp lint + +Check metadata quality. + +```bash +mxcp lint # All endpoints +mxcp lint --severity warning # Warnings only +mxcp lint --severity info # All issues +mxcp lint --json-output +``` + +### mxcp evals + +Test LLM behavior. + +```bash +mxcp evals # All eval suites +mxcp evals safety_checks # Specific suite +mxcp evals --model gpt-4o # Override model +mxcp evals --user-context '{"role": "user"}' +mxcp evals --json-output +``` + +## Data Commands + +### mxcp query + +Execute SQL directly. + +```bash +mxcp query "SELECT * FROM users" +mxcp query "SELECT * FROM sales WHERE region = $region" --param region=US +mxcp query --file query.sql +mxcp query --file query.sql --param date=@dates.json +mxcp query "SELECT COUNT(*) FROM data" --json-output +mxcp query "SELECT * FROM users" --readonly +``` + +### mxcp dbt + +Run dbt commands. + +```bash +mxcp dbt run # Run all models +mxcp dbt run --select model +mxcp dbt test # Run tests +mxcp dbt docs generate # Generate docs +mxcp dbt-config # Generate dbt config +mxcp dbt-config --dry-run # Preview config +``` + +### mxcp drift-snapshot + +Create baseline snapshot. + +```bash +mxcp drift-snapshot # Default profile +mxcp drift-snapshot --profile prod +mxcp drift-snapshot --force # Overwrite existing +mxcp drift-snapshot --dry-run +``` + +### mxcp drift-check + +Check for changes. + +```bash +mxcp drift-check # Use default baseline +mxcp drift-check --baseline path/to/snapshot.json +mxcp drift-check --profile prod +mxcp drift-check --json-output +mxcp drift-check --readonly +``` + +## Monitoring Commands + +### mxcp log + +Query audit logs. + +```bash +# Basic queries +mxcp log # Recent logs +mxcp log --since 1h # Last hour +mxcp log --since 2d # Last 2 days + +# Filtering +mxcp log --tool my_tool # Specific tool +mxcp log --resource my_resource +mxcp log --prompt my_prompt +mxcp log --type tool # By type +mxcp log --status error # Errors only +mxcp log --status success # Successes only +mxcp log --policy deny # Denied by policy + +# Output +mxcp log --limit 50 # Limit results +mxcp log --json # JSON format +mxcp log --export-csv audit.csv +mxcp log --export-duckdb audit.db + +# Combined filters +mxcp log --since 1d --tool my_tool --status error +``` + +### mxcp log-cleanup + +Apply retention policies. + +```bash +mxcp log-cleanup # Apply retention +mxcp log-cleanup --dry-run # Preview deletions +mxcp log-cleanup --profile prod +mxcp log-cleanup --json +``` + +## Common Options + +Available for most commands: + +```bash +--profile PROFILE # Use specific profile +--json-output # Output as JSON +--debug # Debug logging +--readonly # Read-only database access +``` + +## Environment Variables + +```bash +# Config location (use project-local config) +export MXCP_CONFIG=./config.yml +# Or for global config (user manually copies) +# export MXCP_CONFIG=~/.mxcp/config.yml + +# Default profile +export MXCP_PROFILE=production + +# Debug mode +export MXCP_DEBUG=1 + +# Read-only mode +export MXCP_READONLY=1 + +# Override database path +export MXCP_DUCKDB_PATH=/path/to/custom.duckdb + +# Disable analytics +export MXCP_DISABLE_ANALYTICS=1 +``` + +## Time Formats + +For `--since` option: + +```bash +10s # 10 seconds +5m # 5 minutes +2h # 2 hours +1d # 1 day +3w # 3 weeks +``` + +## Exit Codes + +- `0` - Success +- `1` - Error or validation failure +- `2` - Invalid arguments + +## Configuration Options + +### Project Structure Enforcement + +**CRITICAL**: MXCP enforces organized directory structure. Files in wrong locations are **ignored**. + +Required structure: +- Tools: `tools/*.yml` +- Resources: `resources/*.yml` +- Prompts: `prompts/*.yml` +- Python: `python/*.py` +- SQL: `sql/*.sql` + +Use `mxcp init --bootstrap` to create proper structure. + +### Profile Configuration (mxcp-site.yml) + +```yaml +mxcp: 1 +project: my-project + +# Generic SQL tools for database exploration (optional) +sql_tools: + enabled: true + +profiles: + default: + database: + path: data.duckdb + + production: + # Authentication + auth: + provider: github + # OAuth config in project config.yml + + # Audit logging + audit: + enabled: true + path: audit/logs.jsonl + retention_days: 90 + + # OpenTelemetry observability + telemetry: + enabled: true + endpoint: "http://otel-collector:4318" + + # Policy enforcement + policies: + strict_mode: true + + # Database + database: + path: /app/data/production.duckdb +``` + +### Configuration Details + +**Telemetry (OpenTelemetry)**: +```yaml +profiles: + production: + telemetry: + enabled: true + endpoint: "http://localhost:4318" # OTLP endpoint + # Optional: service name + service_name: "my-mxcp-server" +``` + +Provides: +- Distributed tracing for requests +- Performance metrics +- Integration with Jaeger, Grafana, etc. + +**Stateless Mode** (`--stateless` flag): +- For Claude.ai and serverless deployments +- Disables state that persists across requests +- Use for horizontal scaling + +**SQL Tools**: + +Generic SQL tools provide built-in database exploration capabilities for LLMs: +- **`list_tables`** - View all available tables +- **`get_table_schema`** - Examine table structure and columns +- **`execute_sql_query`** - Run custom SQL queries + +Enable via config file (recommended): +```yaml +# mxcp-site.yml +sql_tools: + enabled: true +``` + +Or via command-line flag: +```bash +mxcp serve --sql-tools true # Enable +mxcp serve --sql-tools false # Disable (default) +``` + +**Use cases:** +- Natural language data exploration +- Ad-hoc analysis and discovery +- Prototyping query patterns +- Working with dbt-transformed data + +**Security:** Generic SQL tools allow arbitrary SQL execution. Use read-only database connections and consider policy-based restrictions for production deployments. + +**Example:** See `assets/project-templates/covid_owid/` for complete implementation. + +## Common Workflows + +### Development + +```bash +# Initialize with proper directory structure +mxcp init --bootstrap + +# Validate structure +mxcp validate + +# Test functionality +mxcp test + +# Check documentation +mxcp lint + +# Run locally with debug +mxcp serve --debug +``` + +### Deployment + +```bash +# Create snapshot +mxcp drift-snapshot --profile prod + +# Run tests +mxcp test --profile prod + +# Run evals +mxcp evals --profile prod + +# Deploy +mxcp serve --profile prod +``` + +### Monitoring + +```bash +# Check drift +mxcp drift-check --profile prod + +# View recent errors +mxcp log --since 1h --status error + +# Export audit trail +mxcp log --since 7d --export-duckdb audit.db + +# Clean old logs +mxcp log-cleanup +``` + +## Tips + +1. **Use --debug** for troubleshooting +2. **Test locally** before deployment +3. **Use profiles** for different environments +4. **Export logs** for analysis +5. **Run drift checks** in CI/CD +6. **Validate before committing** +7. **Use --readonly** for query tools diff --git a/skills/mxcp-expert/references/comprehensive-testing-guide.md b/skills/mxcp-expert/references/comprehensive-testing-guide.md new file mode 100644 index 0000000..2057422 --- /dev/null +++ b/skills/mxcp-expert/references/comprehensive-testing-guide.md @@ -0,0 +1,769 @@ +# Comprehensive Testing Guide + +**Complete testing strategy for MXCP servers: MXCP tests, Python unit tests, mocking, test databases, and concurrency safety.** + +## Two Types of Tests + +### 1. MXCP Tests (Integration Tests) + +**Purpose**: Test the full tool/resource/prompt as it will be called by LLMs. + +**Located**: In tool YAML files under `tests:` section + +**Run with**: `mxcp test` + +**Tests**: +- Tool can be invoked with parameters +- Return type matches specification +- Result structure is correct +- Parameter validation works + +**Example**: +```yaml +# tools/get_customers.yml +mxcp: 1 +tool: + name: get_customers + tests: + - name: "basic_query" + arguments: + - key: city + value: "Chicago" + result: + - customer_id: 3 + name: "Bob Johnson" +``` + +### 2. Python Unit Tests (Isolation Tests) + +**Purpose**: Test Python functions in isolation with mocking, edge cases, concurrency. + +**Located**: In `tests/` directory (pytest format) + +**Run with**: `pytest` or `python -m pytest` + +**Tests**: +- Function logic correctness +- Edge cases and error handling +- Mocked external dependencies +- Concurrency safety +- Result correctness verification + +**Example**: +```python +# tests/test_api_wrapper.py +import pytest +from python.api_wrapper import fetch_users + +@pytest.mark.asyncio +async def test_fetch_users_correctness(): + """Test that fetch_users returns correct structure""" + result = await fetch_users(limit=5) + + assert "users" in result + assert "count" in result + assert result["count"] == 5 + assert len(result["users"]) == 5 + assert all("id" in user for user in result["users"]) +``` + +## When to Use Which Tests + +| Scenario | MXCP Tests | Python Unit Tests | +|----------|------------|-------------------| +| SQL-only tool | ✅ Required | ❌ Not applicable | +| Python tool (no external calls) | ✅ Required | ✅ Recommended | +| Python tool (with API calls) | ✅ Required | ✅ **Required** (with mocking) | +| Python tool (with DB writes) | ✅ Required | ✅ **Required** (test DB) | +| Python tool (async/concurrent) | ✅ Required | ✅ **Required** (concurrency tests) | + +## Complete Testing Workflow + +### Phase 1: MXCP Tests (Always First) + +**For every tool, add test cases to YAML:** + +```yaml +tool: + name: my_tool + # ... definition ... + tests: + - name: "happy_path" + arguments: + - key: param1 + value: "test_value" + result: + expected_field: "expected_value" + + - name: "edge_case_empty" + arguments: + - key: param1 + value: "nonexistent" + result: [] + + - name: "missing_optional_param" + arguments: [] + # Should work with defaults +``` + +**Run**: +```bash +mxcp test tool my_tool +``` + +### Phase 2: Python Unit Tests (For Python Tools) + +**Create test file structure**: +```bash +mkdir -p tests +touch tests/__init__.py +touch tests/test_my_module.py +``` + +**Write unit tests with pytest**: +```python +# tests/test_my_module.py +import pytest +from python.my_module import my_function + +def test_my_function_correctness(): + """Verify correct results""" + result = my_function("input") + assert result["key"] == "expected_value" + assert len(result["items"]) == 5 + +def test_my_function_edge_cases(): + """Test edge cases""" + assert my_function("") == {"error": "Empty input"} + assert my_function(None) == {"error": "Invalid input"} +``` + +**Run**: +```bash +pytest tests/ +# Or with coverage +pytest --cov=python tests/ +``` + +## Testing SQL Tools with Test Database + +**CRITICAL**: SQL tools must be tested with real data to verify result correctness. + +### Pattern 1: Use dbt Seeds for Test Data + +```bash +# 1. Create test data seed +cat > seeds/test_data.csv <<'EOF' +id,name,value +1,test1,100 +2,test2,200 +3,test3,300 +EOF + +# 2. Create schema +cat > seeds/schema.yml <<'EOF' +version: 2 +seeds: + - name: test_data + columns: + - name: id + tests: [unique, not_null] +EOF + +# 3. Load test data +dbt seed --select test_data + +# 4. Create tool with tests +cat > tools/query_test_data.yml <<'EOF' +mxcp: 1 +tool: + name: query_test_data + parameters: + - name: min_value + type: integer + return: + type: array + source: + code: | + SELECT * FROM test_data WHERE value >= $min_value + tests: + - name: "filter_200" + arguments: + - key: min_value + value: 200 + result: + - id: 2 + value: 200 + - id: 3 + value: 300 +EOF + +# 5. Test +mxcp test tool query_test_data +``` + +### Pattern 2: Create Test Fixtures in SQL + +```sql +-- models/test_fixtures.sql +{{ config(materialized='table') }} + +-- Create predictable test data +SELECT 1 as id, 'Alice' as name, 100 as score +UNION ALL +SELECT 2 as id, 'Bob' as name, 200 as score +UNION ALL +SELECT 3 as id, 'Charlie' as name, 150 as score +``` + +```yaml +# tools/top_scores.yml +tool: + name: top_scores + source: + code: | + SELECT * FROM test_fixtures ORDER BY score DESC LIMIT $limit + tests: + - name: "top_2" + arguments: + - key: limit + value: 2 + result: + - id: 2 + name: "Bob" + score: 200 + - id: 3 + name: "Charlie" + score: 150 +``` + +### Pattern 3: Verify Aggregation Correctness + +```yaml +# tools/calculate_stats.yml +tool: + name: calculate_stats + source: + code: | + SELECT + COUNT(*) as total_count, + SUM(score) as total_score, + AVG(score) as avg_score, + MAX(score) as max_score + FROM test_fixtures + tests: + - name: "verify_aggregations" + arguments: [] + result: + - total_count: 3 + total_score: 450 + avg_score: 150.0 + max_score: 200 +``` + +**If aggregations don't match expected values, the SQL logic is WRONG.** + +## Testing Python Tools with Mocking + +**CRITICAL**: Python tools with external API calls MUST use mocking in tests. + +### Pattern 1: Mock HTTP Calls with pytest-httpx + +```bash +# Install +pip install pytest-httpx +``` + +```python +# python/api_client.py +import httpx + +async def fetch_external_data(api_key: str, user_id: int) -> dict: + """Fetch data from external API""" + async with httpx.AsyncClient() as client: + response = await client.get( + f"https://api.example.com/users/{user_id}", + headers={"Authorization": f"Bearer {api_key}"} + ) + response.raise_for_status() + return response.json() +``` + +```python +# tests/test_api_client.py +import pytest +from httpx import Response +from python.api_client import fetch_external_data + +@pytest.mark.asyncio +async def test_fetch_external_data_success(httpx_mock): + """Test successful API call with mocked response""" + # Mock the HTTP call + httpx_mock.add_response( + url="https://api.example.com/users/123", + json={"id": 123, "name": "Test User", "email": "test@example.com"} + ) + + # Call function + result = await fetch_external_data("fake_api_key", 123) + + # Verify correctness + assert result["id"] == 123 + assert result["name"] == "Test User" + assert result["email"] == "test@example.com" + +@pytest.mark.asyncio +async def test_fetch_external_data_error(httpx_mock): + """Test API error handling""" + httpx_mock.add_response( + url="https://api.example.com/users/999", + status_code=404, + json={"error": "User not found"} + ) + + # Should handle error gracefully + with pytest.raises(httpx.HTTPStatusError): + await fetch_external_data("fake_api_key", 999) +``` + +### Pattern 2: Mock Database Calls + +```python +# python/db_operations.py +from mxcp.runtime import db + +def get_user_orders(user_id: int) -> list[dict]: + """Get orders for a user""" + result = db.execute( + "SELECT * FROM orders WHERE user_id = $1", + {"user_id": user_id} + ) + return result.fetchall() +``` + +```python +# tests/test_db_operations.py +import pytest +from unittest.mock import Mock, MagicMock +from python.db_operations import get_user_orders + +def test_get_user_orders(monkeypatch): + """Test with mocked database""" + # Create mock result + mock_result = MagicMock() + mock_result.fetchall.return_value = [ + {"order_id": 1, "user_id": 123, "amount": 50.0}, + {"order_id": 2, "user_id": 123, "amount": 75.0} + ] + + # Mock db.execute + mock_db = Mock() + mock_db.execute.return_value = mock_result + + # Inject mock + import python.db_operations + monkeypatch.setattr(python.db_operations, "db", mock_db) + + # Test + orders = get_user_orders(123) + + # Verify + assert len(orders) == 2 + assert orders[0]["order_id"] == 1 + assert sum(o["amount"] for o in orders) == 125.0 +``` + +### Pattern 3: Mock Third-Party Libraries + +```python +# python/stripe_wrapper.py +import stripe + +def create_customer(email: str, name: str) -> dict: + """Create Stripe customer""" + customer = stripe.Customer.create(email=email, name=name) + return {"id": customer.id, "email": customer.email} +``` + +```python +# tests/test_stripe_wrapper.py +import pytest +from unittest.mock import Mock, patch +from python.stripe_wrapper import create_customer + +@patch('stripe.Customer.create') +def test_create_customer(mock_create): + """Test Stripe customer creation with mock""" + # Mock Stripe response + mock_customer = Mock() + mock_customer.id = "cus_test123" + mock_customer.email = "test@example.com" + mock_create.return_value = mock_customer + + # Call function + result = create_customer("test@example.com", "Test User") + + # Verify correctness + assert result["id"] == "cus_test123" + assert result["email"] == "test@example.com" + + # Verify Stripe was called correctly + mock_create.assert_called_once_with( + email="test@example.com", + name="Test User" + ) +``` + +## Result Correctness Verification + +**CRITICAL**: Tests must verify results are CORRECT, not just that code doesn't crash. + +### Bad Test (Only checks structure): +```python +def test_calculate_total_bad(): + result = calculate_total([10, 20, 30]) + assert "total" in result # ❌ Doesn't verify correctness +``` + +### Good Test (Verifies correct value): +```python +def test_calculate_total_good(): + result = calculate_total([10, 20, 30]) + assert result["total"] == 60 # ✅ Verifies correct calculation + assert result["count"] == 3 # ✅ Verifies correct count + assert result["average"] == 20.0 # ✅ Verifies correct average +``` + +### Pattern: Test Edge Cases for Correctness + +```python +def test_aggregation_correctness(): + """Test various aggregations for correctness""" + data = [ + {"id": 1, "value": 100}, + {"id": 2, "value": 200}, + {"id": 3, "value": 150} + ] + + result = aggregate_data(data) + + # Verify each aggregation + assert result["sum"] == 450 # 100 + 200 + 150 + assert result["avg"] == 150.0 # 450 / 3 + assert result["min"] == 100 + assert result["max"] == 200 + assert result["count"] == 3 + + # Verify derived values + assert result["range"] == 100 # 200 - 100 + assert result["median"] == 150 + +def test_empty_data_correctness(): + """Test edge case: empty data""" + result = aggregate_data([]) + + assert result["sum"] == 0 + assert result["avg"] == 0.0 + assert result["count"] == 0 + # Ensure no crashes, correct behavior for empty data +``` + +## Concurrency Safety for Python Tools + +**CRITICAL**: MXCP tools run as a server - multiple requests can happen simultaneously. + +### Common Concurrency Issues + +#### ❌ WRONG: Global State with Race Conditions + +```python +# python/unsafe_counter.py +counter = 0 # ❌ DANGER: Race condition! + +def increment_counter() -> dict: + global counter + counter += 1 # ❌ Not thread-safe! + return {"count": counter} + +# Two simultaneous requests could both read counter=5, +# both increment to 6, both write 6 -> one increment lost! +``` + +#### ✅ CORRECT: Use Thread-Safe Approaches + +**Option 1: Avoid shared state (stateless)** +```python +# python/safe_stateless.py +def process_request(data: dict) -> dict: + """Completely stateless - safe for concurrent calls""" + result = compute_something(data) + return {"result": result} + # No global state, no problem! +``` + +**Option 2: Use thread-safe structures** +```python +# python/safe_with_lock.py +import threading + +counter_lock = threading.Lock() +counter = 0 + +def increment_counter() -> dict: + global counter + with counter_lock: # ✅ Thread-safe + counter += 1 + current = counter + return {"count": current} +``` + +**Option 3: Use atomic operations** +```python +# python/safe_atomic.py +from threading import Lock +from collections import defaultdict + +# Thread-safe counter +class SafeCounter: + def __init__(self): + self._value = 0 + self._lock = Lock() + + def increment(self): + with self._lock: + self._value += 1 + return self._value + +counter = SafeCounter() + +def increment_counter() -> dict: + return {"count": counter.increment()} +``` + +### Concurrency-Safe Patterns + +#### Pattern 1: Database as State (DuckDB is thread-safe) + +```python +# python/db_counter.py +from mxcp.runtime import db + +def increment_counter() -> dict: + """Use database for state - thread-safe""" + db.execute(""" + CREATE TABLE IF NOT EXISTS counter ( + id INTEGER PRIMARY KEY, + value INTEGER + ) + """) + + db.execute(""" + INSERT INTO counter (id, value) VALUES (1, 1) + ON CONFLICT(id) DO UPDATE SET value = value + 1 + """) + + result = db.execute("SELECT value FROM counter WHERE id = 1") + return {"count": result.fetchone()["value"]} +``` + +#### Pattern 2: Local Variables Only (Immutable) + +```python +# python/safe_processing.py +async def process_data(input_data: list[dict]) -> dict: + """Local variables only - safe for concurrent calls""" + # All state is local to this function call + results = [] + total = 0 + + for item in input_data: + processed = transform(item) # Pure function + results.append(processed) + total += processed["value"] + + return { + "results": results, + "total": total, + "count": len(results) + } + # When function returns, all state is discarded +``` + +#### Pattern 3: Async/Await (Concurrent, Not Parallel) + +```python +# python/safe_async.py +import asyncio +import httpx + +async def fetch_multiple_users(user_ids: list[int]) -> list[dict]: + """Concurrent API calls - safe with async""" + + async def fetch_one(user_id: int) -> dict: + # Each call has its own context - no shared state + async with httpx.AsyncClient() as client: + response = await client.get(f"https://api.example.com/users/{user_id}") + return response.json() + + # Run concurrently, but each fetch_one is independent + results = await asyncio.gather(*[fetch_one(uid) for uid in user_ids]) + return results +``` + +### Testing Concurrency Safety + +```python +# tests/test_concurrency.py +import pytest +import asyncio +from python.my_module import concurrent_function + +@pytest.mark.asyncio +async def test_concurrent_calls_no_race_condition(): + """Test that concurrent calls don't have race conditions""" + + # Run function 100 times concurrently + tasks = [concurrent_function(i) for i in range(100)] + results = await asyncio.gather(*tasks) + + # Verify all calls succeeded + assert len(results) == 100 + + # Verify no data corruption + assert all(isinstance(r, dict) for r in results) + + # If function has a counter, verify correctness + # (e.g., if each call increments, final count should be 100) + +def test_parallel_execution_thread_safe(): + """Test with actual threading""" + import threading + + results = [] + errors = [] + + def worker(n): + try: + result = my_function(n) + results.append(result) + except Exception as e: + errors.append(e) + + # Create 50 threads + threads = [threading.Thread(target=worker, args=(i,)) for i in range(50)] + + # Start all threads + for t in threads: + t.start() + + # Wait for completion + for t in threads: + t.join() + + # Verify + assert len(errors) == 0, f"Errors occurred: {errors}" + assert len(results) == 50 +``` + +## Complete Testing Checklist + +### For SQL Tools: + +- [ ] MXCP test cases in YAML +- [ ] Test with real seed data +- [ ] Verify result correctness (exact values) +- [ ] Test edge cases (empty results, NULL values) +- [ ] Test filters work correctly +- [ ] Test aggregations are mathematically correct +- [ ] Test with dbt test for data quality + +### For Python Tools (No External Calls): + +- [ ] MXCP test cases in YAML +- [ ] Python unit tests (pytest) +- [ ] Verify result correctness +- [ ] Test edge cases (empty input, NULL, invalid) +- [ ] Test error handling +- [ ] Test concurrency safety (if using shared state) + +### For Python Tools (With External API Calls): + +- [ ] MXCP test cases in YAML +- [ ] Python unit tests with mocking (pytest + httpx_mock) +- [ ] Mock all external API calls +- [ ] Test success path with mocked responses +- [ ] Test error cases (404, 500, timeout) +- [ ] Verify correct API parameters +- [ ] Test result correctness +- [ ] Test concurrency (multiple simultaneous calls) + +### For Python Tools (With Database Operations): + +- [ ] MXCP test cases in YAML +- [ ] Python unit tests +- [ ] Use test fixtures/seed data +- [ ] Verify query results correctness +- [ ] Test transactions (if applicable) +- [ ] Test concurrency (DuckDB is thread-safe) +- [ ] Clean up test data after tests + +## Project Structure for Testing + +``` +project/ +├── mxcp-site.yml +├── tools/ +│ └── my_tool.yml # Contains MXCP tests +├── python/ +│ └── my_module.py # Python code +├── tests/ +│ ├── __init__.py +│ ├── test_my_module.py # Python unit tests +│ ├── conftest.py # pytest fixtures +│ └── fixtures/ +│ └── test_data.json # Test data +├── seeds/ +│ ├── test_data.csv # Test database seeds +│ └── schema.yml +└── requirements.txt # Include: pytest, pytest-asyncio, pytest-httpx, pytest-cov +``` + +## Running Tests + +```bash +# 1. MXCP tests (always run first) +mxcp validate # Structure validation +mxcp test # Integration tests + +# 2. dbt tests (if using dbt) +dbt test + +# 3. Python unit tests +pytest tests/ -v + +# 4. With coverage report +pytest tests/ --cov=python --cov-report=html + +# 5. Concurrency stress test (custom) +pytest tests/test_concurrency.py -v --count=100 + +# All together +mxcp validate && mxcp test && dbt test && pytest tests/ -v +``` + +## Summary + +**Both types of tests are required**: + +1. **MXCP tests** - Verify tools work end-to-end +2. **Python unit tests** - Verify logic, mocking, correctness, concurrency + +**Key principles**: +- ✅ **Mock all external calls** - Use pytest-httpx, unittest.mock +- ✅ **Verify result correctness** - Don't just check structure +- ✅ **Use test databases** - SQL tools need real data +- ✅ **Test concurrency** - Tools run as servers +- ✅ **Avoid global mutable state** - Use stateless patterns or locks +- ✅ **Test edge cases** - Empty data, NULL, invalid input + +**Before declaring a project done, BOTH test types must pass completely.** diff --git a/skills/mxcp-expert/references/database-connections.md b/skills/mxcp-expert/references/database-connections.md new file mode 100644 index 0000000..d4258f9 --- /dev/null +++ b/skills/mxcp-expert/references/database-connections.md @@ -0,0 +1,842 @@ +# Database Connections Guide + +Complete guide for connecting MXCP to external databases (PostgreSQL, MySQL, SQLite, SQL Server) using DuckDB's ATTACH functionality and dbt integration. + +## Overview + +MXCP can connect to external databases in two ways: +1. **Direct querying** via DuckDB ATTACH (read data from external databases) +2. **dbt integration** (transform external data using dbt sources and models) + +**Key principle**: External databases → DuckDB (via ATTACH or dbt) → MXCP tools + +## When to Use Database Connections + +**Use database connections when**: +- You have existing data in PostgreSQL, MySQL, or other SQL databases +- You want to query production databases (read-only recommended) +- You need to join external data with local data +- You want to cache/materialize external data locally + +**Don't use database connections when**: +- You can export data to CSV (use dbt seeds instead - simpler and safer) +- You need real-time writes (MXCP is read-focused) +- The database has complex security requirements (use API wrapper instead) + +## Method 1: Direct Database Access with ATTACH + +### PostgreSQL Connection + +#### Basic ATTACH Syntax + +```sql +-- Attach PostgreSQL database +INSTALL postgres; +LOAD postgres; + +ATTACH 'host=localhost port=5432 dbname=mydb user=myuser password=mypass' + AS postgres_db (TYPE POSTGRES); + +-- Query attached database +SELECT * FROM postgres_db.public.customers WHERE country = 'US'; +``` + +#### Complete Working Example + +**Project structure**: +``` +postgres-query/ +├── mxcp-site.yml +├── config.yml # Database credentials +├── tools/ +│ ├── query_customers.yml +│ └── get_orders.yml +└── sql/ + └── setup.sql # ATTACH commands +``` + +**Step 1: Create config.yml with database credentials** + +```yaml +# config.yml (in project directory) +mxcp: 1 + +profiles: + default: + secrets: + - name: postgres_connection + type: env + parameters: + env_var: POSTGRES_CONNECTION_STRING + + # Alternative: separate credentials + - name: db_host + type: env + parameters: + env_var: DB_HOST + - name: db_user + type: env + parameters: + env_var: DB_USER + - name: db_password + type: env + parameters: + env_var: DB_PASSWORD +``` + +**Step 2: Set environment variables** + +```bash +# Option 1: Connection string +export POSTGRES_CONNECTION_STRING="host=localhost port=5432 dbname=mydb user=myuser password=mypass" + +# Option 2: Separate credentials +export DB_HOST="localhost" +export DB_USER="myuser" +export DB_PASSWORD="mypass" +``` + +**Step 3: Create SQL setup file** + +```sql +-- sql/setup.sql +-- Install and load PostgreSQL extension +INSTALL postgres; +LOAD postgres; + +-- Attach database (connection string from environment) +ATTACH 'host=${DB_HOST} port=5432 dbname=mydb user=${DB_USER} password=${DB_PASSWORD}' + AS prod_db (TYPE POSTGRES); +``` + +**Step 4: Create query tool** + +```yaml +# tools/query_customers.yml +mxcp: 1 +tool: + name: query_customers + description: "Query customers from PostgreSQL database by country" + parameters: + - name: country + type: string + description: "Filter by country code (e.g., 'US', 'UK')" + required: false + return: + type: array + items: + type: object + properties: + customer_id: { type: integer } + name: { type: string } + email: { type: string } + country: { type: string } + source: + code: | + -- First ensure PostgreSQL is attached + INSTALL postgres; + LOAD postgres; + ATTACH IF NOT EXISTS 'host=${DB_HOST} port=5432 dbname=mydb user=${DB_USER} password=${DB_PASSWORD}' + AS prod_db (TYPE POSTGRES); + + -- Query attached database + SELECT + customer_id, + name, + email, + country + FROM prod_db.public.customers + WHERE $country IS NULL OR country = $country + ORDER BY customer_id + LIMIT 1000 + tests: + - name: "test_connection" + arguments: [] + # Test will verify connection works +``` + +**Step 5: Validate and test** + +```bash +# Set credentials +export DB_HOST="localhost" +export DB_USER="myuser" +export DB_PASSWORD="mypass" + +# Validate structure +mxcp validate + +# Test tool +mxcp run tool query_customers --param country="US" + +# Start server +mxcp serve +``` + +### MySQL Connection + +```sql +-- Install MySQL extension +INSTALL mysql; +LOAD mysql; + +-- Attach MySQL database +ATTACH 'host=localhost port=3306 database=mydb user=root password=pass' + AS mysql_db (TYPE MYSQL); + +-- Query +SELECT * FROM mysql_db.orders WHERE order_date >= '2024-01-01'; +``` + +**Complete tool example**: + +```yaml +# tools/query_mysql_orders.yml +mxcp: 1 +tool: + name: query_mysql_orders + description: "Query orders from MySQL database" + parameters: + - name: start_date + type: string + format: date + required: false + - name: status + type: string + required: false + return: + type: array + items: + type: object + source: + code: | + INSTALL mysql; + LOAD mysql; + ATTACH IF NOT EXISTS 'host=${MYSQL_HOST} database=${MYSQL_DB} user=${MYSQL_USER} password=${MYSQL_PASSWORD}' + AS mysql_db (TYPE MYSQL); + + SELECT + order_id, + customer_id, + order_date, + total_amount, + status + FROM mysql_db.orders + WHERE ($start_date IS NULL OR order_date >= $start_date) + AND ($status IS NULL OR status = $status) + ORDER BY order_date DESC + LIMIT 1000 +``` + +### SQLite Connection + +```sql +-- Attach SQLite database +ATTACH 'path/to/database.db' AS sqlite_db (TYPE SQLITE); + +-- Query +SELECT * FROM sqlite_db.users WHERE active = true; +``` + +**Tool example**: + +```yaml +# tools/query_sqlite.yml +mxcp: 1 +tool: + name: query_sqlite_users + description: "Query users from SQLite database" + parameters: + - name: active_only + type: boolean + default: true + return: + type: array + source: + code: | + ATTACH IF NOT EXISTS '${SQLITE_DB_PATH}' AS sqlite_db (TYPE SQLITE); + + SELECT user_id, username, email, created_at + FROM sqlite_db.users + WHERE $active_only = false OR active = true + ORDER BY created_at DESC +``` + +### SQL Server Connection + +```sql +-- Install SQL Server extension +INSTALL sqlserver; +LOAD sqlserver; + +-- Attach SQL Server database +ATTACH 'Server=localhost;Database=mydb;Uid=user;Pwd=pass;' + AS sqlserver_db (TYPE SQLSERVER); + +-- Query +SELECT * FROM sqlserver_db.dbo.products WHERE category = 'Electronics'; +``` + +## Method 2: dbt Integration with External Databases + +**Use dbt when**: +- You want to materialize/cache external data locally +- You need to transform external data before querying +- You want data quality tests on external data +- You prefer declarative SQL over ATTACH statements + +### dbt Sources for External Databases + +**Pattern**: External DB → dbt source → dbt model → MXCP tool + +#### Step 1: Configure dbt profile for external database + +```yaml +# profiles.yml (auto-generated by MXCP, or manually edit) +my_project: + outputs: + dev: + type: postgres # or mysql, sqlserver, etc. + host: localhost + port: 5432 + user: "{{ env_var('DB_USER') }}" + password: "{{ env_var('DB_PASSWORD') }}" + dbname: mydb + schema: public + threads: 4 + + # Hybrid: use DuckDB for local, Postgres for source + hybrid: + type: duckdb + path: "{{ env_var('MXCP_DUCKDB_PATH', 'data/db-default.duckdb') }}" + target: hybrid +``` + +#### Step 2: Define external database as dbt source + +```yaml +# models/sources.yml +version: 2 + +sources: + - name: production_db + description: "Production PostgreSQL database" + database: postgres_db # Matches ATTACH name + schema: public + tables: + - name: customers + description: "Customer master data" + columns: + - name: customer_id + description: "Unique customer identifier" + tests: + - unique + - not_null + - name: email + tests: + - not_null + - name: country + tests: + - not_null + + - name: orders + description: "Order transactions" + columns: + - name: order_id + tests: + - unique + - not_null + - name: customer_id + tests: + - not_null + - relationships: + to: source('production_db', 'customers') + field: customer_id +``` + +#### Step 3: Create dbt model to cache/transform external data + +```sql +-- models/customer_summary.sql +{{ config( + materialized='table', + description='Customer summary from production database' +) }} + +SELECT + c.customer_id, + c.name, + c.email, + c.country, + COUNT(o.order_id) as order_count, + COALESCE(SUM(o.total_amount), 0) as total_spent, + MAX(o.order_date) as last_order_date +FROM {{ source('production_db', 'customers') }} c +LEFT JOIN {{ source('production_db', 'orders') }} o + ON c.customer_id = o.customer_id +GROUP BY c.customer_id, c.name, c.email, c.country +``` + +```yaml +# models/schema.yml +version: 2 + +models: + - name: customer_summary + description: "Aggregated customer metrics from production" + columns: + - name: customer_id + tests: + - unique + - not_null + - name: order_count + tests: + - not_null + - name: total_spent + tests: + - not_null +``` + +#### Step 4: Run dbt to materialize data + +```bash +# Test connection to external database +dbt debug + +# Run models (fetches from external DB, materializes in DuckDB) +dbt run --select customer_summary + +# Test data quality +dbt test --select customer_summary +``` + +#### Step 5: Create MXCP tool to query materialized data + +```yaml +# tools/get_customer_summary.yml +mxcp: 1 +tool: + name: get_customer_summary + description: "Get customer summary statistics from cached production data" + parameters: + - name: country + type: string + required: false + - name: min_orders + type: integer + default: 0 + return: + type: array + items: + type: object + properties: + customer_id: { type: integer } + name: { type: string } + order_count: { type: integer } + total_spent: { type: number } + source: + code: | + SELECT + customer_id, + name, + email, + country, + order_count, + total_spent, + last_order_date + FROM customer_summary + WHERE ($country IS NULL OR country = $country) + AND order_count >= $min_orders + ORDER BY total_spent DESC + LIMIT 100 +``` + +#### Step 6: Refresh data periodically + +```bash +# Manual refresh +dbt run --select customer_summary + +# Or create Python tool to trigger refresh +``` + +```yaml +# tools/refresh_data.yml +mxcp: 1 +tool: + name: refresh_customer_data + description: "Refresh customer summary from production database" + language: python + return: + type: object + source: + file: ../python/refresh.py +``` + +```python +# python/refresh.py +from mxcp.runtime import reload_duckdb +import subprocess + +def refresh_customer_data() -> dict: + """Refresh customer summary from external database""" + + def run_dbt(): + result = subprocess.run( + ["dbt", "run", "--select", "customer_summary"], + capture_output=True, + text=True + ) + if result.returncode != 0: + raise Exception(f"dbt run failed: {result.stderr}") + + test_result = subprocess.run( + ["dbt", "test", "--select", "customer_summary"], + capture_output=True, + text=True + ) + if test_result.returncode != 0: + raise Exception(f"dbt test failed: {test_result.stderr}") + + # Run dbt with exclusive database access + reload_duckdb( + payload_func=run_dbt, + description="Refreshing customer data from production" + ) + + return { + "status": "success", + "message": "Customer data refreshed from production database" + } +``` + +### Incremental dbt Models for Large Tables + +For large external tables, use incremental materialization: + +```sql +-- models/orders_incremental.sql +{{ config( + materialized='incremental', + unique_key='order_id', + on_schema_change='fail' +) }} + +SELECT + order_id, + customer_id, + order_date, + total_amount, + status +FROM {{ source('production_db', 'orders') }} + +{% if is_incremental() %} + -- Only fetch new/updated orders + WHERE order_date > (SELECT MAX(order_date) FROM {{ this }}) +{% endif %} +``` + +```bash +# First run: fetch all historical data +dbt run --select orders_incremental --full-refresh + +# Subsequent runs: only fetch new data +dbt run --select orders_incremental +``` + +## Connection Patterns and Best Practices + +### Pattern 1: Read-Only Querying + +**Use case**: Query production database directly without caching + +```yaml +tool: + name: query_live_data + source: + code: | + ATTACH IF NOT EXISTS 'connection_string' AS prod (TYPE POSTGRES); + SELECT * FROM prod.public.table WHERE ... +``` + +**Pros**: Always fresh data +**Cons**: Slower queries, database load + +### Pattern 2: Cached/Materialized Data + +**Use case**: Cache external data in DuckDB for fast queries + +```sql +-- dbt model caches external data +SELECT * FROM {{ source('external_db', 'table') }} +``` + +```yaml +# MXCP tool queries cache +tool: + source: + code: SELECT * FROM cached_table WHERE ... +``` + +**Pros**: Fast queries, no database load +**Cons**: Data staleness, needs refresh + +### Pattern 3: Hybrid (Cache + Live) + +**Use case**: Cache most data, query live for real-time needs + +```sql +-- Combine cached and live data +SELECT * FROM cached_historical_orders +UNION ALL +SELECT * FROM prod.public.orders WHERE order_date >= CURRENT_DATE - INTERVAL '7 days' +``` + +### Security Best Practices + +#### 1. Use Read-Only Database Users + +```sql +-- PostgreSQL: Create read-only user +CREATE USER readonly_user WITH PASSWORD 'secure_password'; +GRANT CONNECT ON DATABASE mydb TO readonly_user; +GRANT USAGE ON SCHEMA public TO readonly_user; +GRANT SELECT ON ALL TABLES IN SCHEMA public TO readonly_user; +``` + +#### 2. Store Credentials in Secrets + +```yaml +# config.yml - NEVER commit passwords +secrets: + - name: db_password + type: env + parameters: + env_var: DB_PASSWORD + + # Production: use Vault + - name: prod_db_password + type: vault + parameters: + path: secret/data/myapp/database + field: password +``` + +#### 3. Use Connection Pooling (for Python approach) + +```python +# python/db_client.py +from mxcp.runtime import on_init, on_shutdown +import psycopg2.pool + +connection_pool = None + +@on_init +def setup_pool(): + global connection_pool + connection_pool = psycopg2.pool.SimpleConnectionPool( + minconn=1, + maxconn=5, + host=os.getenv("DB_HOST"), + database=os.getenv("DB_NAME"), + user=os.getenv("DB_USER"), + password=os.getenv("DB_PASSWORD") + ) + +@on_shutdown +def close_pool(): + global connection_pool + if connection_pool: + connection_pool.closeall() + +def query_database(sql: str) -> list[dict]: + conn = connection_pool.getconn() + try: + cursor = conn.cursor() + cursor.execute(sql) + results = cursor.fetchall() + return results + finally: + connection_pool.putconn(conn) +``` + +### Error Handling + +#### Handle Connection Failures + +```yaml +# tools/query_with_error_handling.yml +tool: + name: safe_query + language: python + source: + file: ../python/safe_query.py +``` + +```python +# python/safe_query.py +from mxcp.runtime import db +import duckdb + +def safe_query(table_name: str) -> dict: + """Query external database with error handling""" + try: + # Try to attach if not already attached + db.execute(""" + INSTALL postgres; + LOAD postgres; + ATTACH IF NOT EXISTS 'host=${DB_HOST} dbname=${DB_NAME} user=${DB_USER} password=${DB_PASSWORD}' + AS prod (TYPE POSTGRES); + """) + + # Query + results = db.execute(f"SELECT * FROM prod.public.{table_name} LIMIT 100").fetchall() + + return { + "success": True, + "row_count": len(results), + "data": results + } + + except duckdb.CatalogException as e: + return { + "success": False, + "error": "Table not found", + "message": f"Table {table_name} does not exist in external database", + "suggestion": "Check table name and database connection" + } + + except duckdb.IOException as e: + return { + "success": False, + "error": "Connection failed", + "message": "Could not connect to external database", + "suggestion": "Check database credentials and network connectivity" + } + + except Exception as e: + return { + "success": False, + "error": "Unexpected error", + "message": str(e) + } +``` + +### Performance Optimization + +#### 1. Add Indexes on Frequently Filtered Columns + +```sql +-- On external database (PostgreSQL) +CREATE INDEX idx_customers_country ON customers(country); +CREATE INDEX idx_orders_date ON orders(order_date); +``` + +#### 2. Limit Result Sets + +```sql +-- Always add LIMIT for large tables +SELECT * FROM prod.public.orders +WHERE order_date >= '2024-01-01' +LIMIT 1000 -- Prevent overwhelming queries +``` + +#### 3. Materialize Complex Joins + +```sql +-- Instead of complex join on every query +-- Create dbt model to materialize the join +{{ config(materialized='table') }} + +SELECT ... complex join logic ... +FROM {{ source('prod', 'table1') }} t1 +JOIN {{ source('prod', 'table2') }} t2 ... +``` + +## Complete Example: PostgreSQL to MXCP + +**Scenario**: Query production PostgreSQL customer database + +```bash +# 1. Create project +mkdir postgres-customers && cd postgres-customers +mxcp init --bootstrap + +# 2. Create config +cat > config.yml <<'EOF' +mxcp: 1 + +profiles: + default: + secrets: + - name: db_host + type: env + parameters: + env_var: DB_HOST + - name: db_user + type: env + parameters: + env_var: DB_USER + - name: db_password + type: env + parameters: + env_var: DB_PASSWORD +EOF + +# 3. Create tool +cat > tools/query_customers.yml <<'EOF' +mxcp: 1 +tool: + name: query_customers + description: "Query customers from PostgreSQL" + parameters: + - name: country + type: string + required: false + return: + type: array + source: + code: | + INSTALL postgres; + LOAD postgres; + ATTACH IF NOT EXISTS 'host=${DB_HOST} port=5432 dbname=customers user=${DB_USER} password=${DB_PASSWORD}' + AS prod (TYPE POSTGRES); + + SELECT customer_id, name, email, country + FROM prod.public.customers + WHERE $country IS NULL OR country = $country + LIMIT 100 +EOF + +# 4. Set credentials +export DB_HOST="localhost" +export DB_USER="readonly_user" +export DB_PASSWORD="secure_password" + +# 5. Test +mxcp validate +mxcp run tool query_customers --param country="US" + +# 6. Start server +mxcp serve +``` + +## Summary + +**For external database connections**: + +1. **Direct querying** → Use ATTACH with parameterized connection strings +2. **Cached data** → Use dbt sources + models for materialization +3. **Always use read-only users** for security +4. **Store credentials in environment variables** or Vault +5. **Handle connection errors** gracefully in Python tools +6. **Test with** `mxcp validate && mxcp run tool ` +7. **Use dbt for** large tables (incremental models) and transformations + +**Decision guide**: +- Small queries, real-time data needed → ATTACH +- Large tables, can tolerate staleness → dbt materialization +- Complex transformations → dbt models +- Simple SELECT queries → ATTACH + +This approach gives you full SQL database access while maintaining MXCP's security, validation, and testing workflow. diff --git a/skills/mxcp-expert/references/dbt-core-guide.md b/skills/mxcp-expert/references/dbt-core-guide.md new file mode 100644 index 0000000..9c17179 --- /dev/null +++ b/skills/mxcp-expert/references/dbt-core-guide.md @@ -0,0 +1,498 @@ +# dbt Core Guide for MXCP + +Essential dbt (data build tool) knowledge for building MXCP servers. + +## What is dbt? + +dbt (data build tool) is a transformation workflow tool that enables data analysts and engineers to transform data in their data warehouse using SQL SELECT statements. In MXCP, dbt transforms raw data into clean, queryable tables that MXCP endpoints can access. + +**Core principle**: dbt creates the tables → MXCP queries them + +## Core Concepts + +### 1. Seeds + +**Seeds are CSV files** that dbt loads into your database as tables. They are perfect for: +- Static reference data (country codes, status mappings, etc.) +- Small lookup tables (<10,000 rows) +- User-provided data files that need to be queried + +**Location**: Place CSV files in `seeds/` directory + +**Loading seeds**: +```bash +dbt seed # Load all seeds +dbt seed --select my_file # Load specific seed +mxcp dbt seed # Load via MXCP +``` + +**Example use case**: User provides `customers.csv` → dbt loads it as a table → MXCP tools query it + +**Critical for MXCP**: Seeds are the primary way to make CSV files queryable via MXCP tools. + +### 2. Models + +**Models transform data** using either SQL or Python. Each `.sql` or `.py` file in `models/` becomes a table or view. + +#### SQL Models + +**SQL models are SELECT statements** that transform data. Best for standard transformations, aggregations, and joins. + +**Basic SQL model** (`models/customer_summary.sql`): +```sql +{{ config(materialized='table') }} + +SELECT + customer_id, + COUNT(*) as order_count, + SUM(amount) as total_spent +FROM {{ ref('orders') }} +GROUP BY customer_id +``` + +#### Python Models + +**Python models use pandas** for complex data processing. Best for Excel files, ML preprocessing, and complex transformations. + +**Basic Python model** (`models/process_data.py`): +```python +import pandas as pd + +def model(dbt, session): + # Load data from dbt ref or read files + # df = dbt.ref('source_table').to_pandas() # From dbt source + df = pd.read_excel('data/input.xlsx') # From file + + # Transform using pandas + df = df.dropna(how='all') + df['new_column'] = df['amount'] * 1.1 + + return df # Returns DataFrame that becomes a table +``` + +**When to use Python models:** +- Processing Excel files with complex formatting +- Data cleaning requiring pandas operations (pivoting, melting, etc.) +- ML feature engineering or preprocessing +- Complex string manipulation or regex operations +- Integration with Python libraries (sklearn, numpy, etc.) + +**Materialization types** (for both SQL and Python models): +- `table` - Creates a table (fast queries, slower builds) +- `view` - Creates a view (slow queries, instant builds) - Not available for Python models +- `incremental` - Appends new data only (best for large datasets) + +### 3. Schema Files (schema.yml) + +**Schema files define structure, tests, and documentation** for seeds and models. + +**ALWAYS create schema.yml files** - they are critical for: +- Type validation +- Data quality tests +- Documentation +- Column descriptions + +**Example** (`seeds/schema.yml`): +```yaml +version: 2 + +seeds: + - name: customers + description: "Customer master data from CSV upload" + columns: + - name: customer_id + description: "Unique customer identifier" + tests: + - unique + - not_null + - name: email + description: "Customer email address" + tests: + - not_null + - name: created_at + description: "Account creation timestamp" + data_type: timestamp +``` + +**Example** (`models/schema.yml`): +```yaml +version: 2 + +models: + - name: customer_summary + description: "Aggregated customer metrics" + columns: + - name: customer_id + tests: + - unique + - not_null + - name: total_spent + tests: + - not_null + - name: order_count + tests: + - not_null +``` + +### 4. Sources + +**Sources represent raw data** already in your database (not managed by dbt). + +**Example** (`models/sources.yml`): +```yaml +version: 2 + +sources: + - name: raw_data + tables: + - name: transactions + description: "Raw transaction data" + - name: users + description: "User accounts" +``` + +**Use in models**: +```sql +SELECT * FROM {{ source('raw_data', 'transactions') }} +``` + +## MXCP + dbt Workflow + +### Pattern 1: CSV File to MXCP Tool + +**User request**: "I need to query my sales.csv file" + +**Steps**: + +1. **Create seed** - Place `sales.csv` in `seeds/` directory +2. **Create schema** - Define structure in `seeds/schema.yml`: + ```yaml + version: 2 + + seeds: + - name: sales + description: "Sales data from CSV upload" + columns: + - name: sale_id + tests: [unique, not_null] + - name: amount + tests: [not_null] + - name: sale_date + data_type: date + tests: [not_null] + - name: region + tests: [not_null] + ``` + +3. **Load seed**: + ```bash + dbt seed --select sales + ``` + +4. **Create MXCP tool** (`tools/get_sales.yml`): + ```yaml + mxcp: 1 + tool: + name: get_sales + description: "Query sales data by region and date range" + parameters: + - name: region + type: string + required: false + - name: start_date + type: string + format: date + required: false + - name: end_date + type: string + format: date + required: false + return: + type: array + items: + type: object + source: + code: | + SELECT * FROM sales + WHERE ($region IS NULL OR region = $region) + AND ($start_date IS NULL OR sale_date >= $start_date) + AND ($end_date IS NULL OR sale_date <= $end_date) + ORDER BY sale_date DESC + ``` + +5. **Test**: + ```bash + dbt test --select sales + mxcp validate + mxcp test tool get_sales + ``` + +### Pattern 2: Transform Then Query + +**User request**: "Analyze monthly sales trends from my CSV" + +1. **Seed the raw data** (as above) +2. **Create transformation model** (`models/monthly_sales.sql`): + ```sql + {{ config(materialized='table') }} + + SELECT + region, + DATE_TRUNC('month', sale_date) as month, + SUM(amount) as total_sales, + COUNT(*) as transaction_count, + AVG(amount) as avg_sale + FROM {{ ref('sales') }} + GROUP BY region, month + ``` + +3. **Create schema** (`models/schema.yml`): + ```yaml + version: 2 + + models: + - name: monthly_sales + description: "Monthly sales aggregations" + columns: + - name: region + tests: [not_null] + - name: month + tests: [not_null] + - name: total_sales + tests: [not_null] + ``` + +4. **Run dbt**: + ```bash + dbt seed + dbt run --select monthly_sales + dbt test --select monthly_sales + ``` + +5. **Create MXCP tool** to query the model: + ```yaml + tool: + name: monthly_trends + source: + code: | + SELECT * FROM monthly_sales + WHERE region = $region + ORDER BY month DESC + ``` + +### Pattern 3: Excel Processing with Python Models + +**User request**: "Process this Excel file with multiple sheets and complex formatting" + +1. **Create Python model** (`models/process_excel.py`): + ```python + import pandas as pd + + def model(dbt, session): + # Read Excel file + df = pd.read_excel('data/sales_data.xlsx', sheet_name='Sales') + + # Clean data + df = df.dropna(how='all') # Remove empty rows + df = df.dropna(axis=1, how='all') # Remove empty columns + + # Normalize column names + df.columns = df.columns.str.lower().str.replace(' ', '_') + + # Complex transformations using pandas + df['sale_date'] = pd.to_datetime(df['sale_date']) + df['month'] = df['sale_date'].dt.to_period('M').astype(str) + + # Aggregate data + result = df.groupby(['region', 'month']).agg({ + 'amount': 'sum', + 'quantity': 'sum' + }).reset_index() + + return result + ``` + +2. **Create schema** (`models/schema.yml`): + ```yaml + version: 2 + + models: + - name: process_excel + description: "Processed sales data from Excel" + config: + materialized: table + columns: + - name: region + tests: [not_null] + - name: month + tests: [not_null] + - name: amount + tests: [not_null] + ``` + +3. **Run the Python model**: + ```bash + dbt run --select process_excel + dbt test --select process_excel + ``` + +4. **Create MXCP tool** to query: + ```yaml + mxcp: 1 + tool: + name: get_sales_by_region + description: "Get sales data processed from Excel" + parameters: + - name: region + type: string + default: null + source: + code: | + SELECT * FROM process_excel + WHERE $region IS NULL OR region = $region + ORDER BY month DESC + ``` + +## Project Structure + +``` +mxcp-project/ +├── mxcp-site.yml # MXCP configuration +├── dbt_project.yml # dbt configuration +├── seeds/ # CSV files +│ ├── customers.csv +│ └── schema.yml # Seed schemas (REQUIRED) +├── models/ # SQL transformations +│ ├── staging/ +│ ├── intermediate/ +│ ├── marts/ +│ └── schema.yml # Model schemas (REQUIRED) +├── tools/ # MXCP tools that query seeds/models +└── target/ # dbt build output (gitignored) +``` + +## dbt Commands for MXCP + +```bash +# Initialize dbt project +dbt init + +# Load CSV seeds into database +dbt seed # Load all seeds +dbt seed --select sales # Load specific seed + +# Run transformations +dbt run # Run all models +dbt run --select model_name # Run specific model +dbt run --select +model_name # Run model and upstream dependencies + +# Test data quality +dbt test # Run all tests +dbt test --select sales # Test specific seed/model + +# Documentation +dbt docs generate # Generate documentation +dbt docs serve # Serve documentation site + +# Via MXCP wrapper +mxcp dbt seed +mxcp dbt run +mxcp dbt test +``` + +## Schema.yml Best Practices + +**ALWAYS include these in schema.yml**: + +1. **Version declaration**: `version: 2` +2. **Description for every seed/model**: Helps LLMs and humans understand purpose +3. **Column-level descriptions**: Document what each field contains +4. **Data type declarations**: Ensure proper typing (`data_type: timestamp`, etc.) +5. **Tests for key columns**: + - `unique` - No duplicates + - `not_null` - Required field + - `accepted_values` - Enum validation + - `relationships` - Foreign key validation + +**Example comprehensive schema.yml**: +```yaml +version: 2 + +seeds: + - name: employees + description: "Employee master data" + columns: + - name: employee_id + description: "Unique employee identifier" + data_type: varchar + tests: + - unique + - not_null + - name: department + description: "Department code" + data_type: varchar + tests: + - not_null + - accepted_values: + values: ['engineering', 'sales', 'marketing'] + - name: salary + description: "Annual salary in USD" + data_type: decimal + tests: + - not_null + - name: hire_date + description: "Date of hire" + data_type: date + tests: + - not_null +``` + +## DuckDB Integration + +MXCP uses **DuckDB** as its default database. dbt can target DuckDB directly. + +**Auto-configured by MXCP** - no manual setup needed: +```yaml +# profiles.yml (auto-generated) +my_project: + outputs: + dev: + type: duckdb + path: "{{ env_var('MXCP_DUCKDB_PATH', 'data/db-default.duckdb') }}" + target: dev +``` + +**DuckDB reads CSVs directly**: +```sql +-- In dbt models, you can read CSVs without seeding +SELECT * FROM read_csv_auto('path/to/file.csv') +``` + +**But prefer seeds for user data** - they provide version control and validation. + +## Common Issues + +**Issue**: Seed file not loading +**Solution**: Check CSV format, ensure no special characters in filename, verify schema.yml exists + +**Issue**: Model not found +**Solution**: Run `dbt compile` to check for syntax errors, ensure model is in `models/` directory + +**Issue**: Tests failing +**Solution**: Review test output, check data quality, adjust tests or fix data + +**Issue**: Type errors +**Solution**: Add explicit `data_type` declarations in schema.yml + +## Summary for MXCP Builders + +When building MXCP servers: + +1. **For CSV files** → Use dbt seeds +2. **Always create** `schema.yml` files with tests and types +3. **Load with** `dbt seed` +4. **Transform with** dbt models if needed +5. **Query from** MXCP tools using `SELECT * FROM ` +6. **Validate with** `dbt test` before deploying + +This ensures data quality, type safety, and proper documentation for all data sources. diff --git a/skills/mxcp-expert/references/dbt-patterns.md b/skills/mxcp-expert/references/dbt-patterns.md new file mode 100644 index 0000000..f3d7418 --- /dev/null +++ b/skills/mxcp-expert/references/dbt-patterns.md @@ -0,0 +1,311 @@ +# dbt Integration Patterns + +Guide to combining dbt with MXCP for data transformation pipelines. + +## Why dbt + MXCP? + +**dbt creates the tables → MXCP queries them** + +This pattern provides: +- Data transformation and quality in dbt +- Fast local caching of external data +- SQL queries against materialized views +- Consistent data contracts + +## Setup + +### 1. Enable dbt in MXCP + +```yaml +# mxcp-site.yml +dbt: + enabled: true + model_paths: ["models"] +``` + +### 2. Create dbt Project + +```bash +dbt init +``` + +### 3. Configure dbt Profile + +```yaml +# profiles.yml (auto-generated by mxcp dbt-config) +covid_owid: + outputs: + dev: + type: duckdb + path: data.duckdb + target: dev +``` + +## Basic Pattern + +### dbt Model + +Create `models/sales_summary.sql`: +```sql +{{ config(materialized='table') }} + +SELECT + region, + DATE_TRUNC('month', sale_date) as month, + SUM(amount) as total_sales, + COUNT(*) as transaction_count +FROM {{ source('raw', 'sales_data') }} +GROUP BY region, month +``` + +### Run dbt + +```bash +mxcp dbt run +# or directly: dbt run +``` + +### MXCP Tool Queries Table + +Create `tools/monthly_sales.yml`: +```yaml +mxcp: 1 +tool: + name: monthly_sales + description: "Get monthly sales summary" + parameters: + - name: region + type: string + return: + type: array + source: + code: | + SELECT * FROM sales_summary + WHERE region = $region + ORDER BY month DESC +``` + +## External Data Caching + +### Fetch and Cache External Data + +```sql +-- models/covid_data.sql +{{ config(materialized='table') }} + +SELECT * +FROM read_csv_auto('https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv') +``` + +Run once to cache: +```bash +mxcp dbt run +``` + +### Query Cached Data + +```yaml +# tools/covid_stats.yml +tool: + name: covid_stats + source: + code: | + SELECT location, date, total_cases, new_cases + FROM covid_data + WHERE location = $country + ORDER BY date DESC + LIMIT 30 +``` + +## Incremental Models + +### Incremental Updates + +```sql +-- models/events_incremental.sql +{{ config( + materialized='incremental', + unique_key='event_id' +) }} + +SELECT * +FROM read_json('https://api.example.com/events') + +{% if is_incremental() %} +WHERE created_at > (SELECT MAX(created_at) FROM {{ this }}) +{% endif %} +``` + +## Sources and References + +### Define Sources + +```yaml +# models/sources.yml +version: 2 + +sources: + - name: raw + tables: + - name: sales_data + - name: customer_data +``` + +### Reference Models + +```sql +-- models/customer_summary.sql +{{ config(materialized='table') }} + +WITH customers AS ( + SELECT * FROM {{ source('raw', 'customer_data') }} +), +sales AS ( + SELECT * FROM {{ ref('sales_summary') }} +) +SELECT + c.customer_id, + c.name, + s.total_sales +FROM customers c +JOIN sales s ON c.customer_id = s.customer_id +``` + +## Data Quality Tests + +### dbt Tests + +```yaml +# models/schema.yml +version: 2 + +models: + - name: sales_summary + columns: + - name: region + tests: + - not_null + - name: total_sales + tests: + - not_null + - positive_value + - name: month + tests: + - unique +``` + +### Run Tests + +```bash +mxcp dbt test +``` + +## Complete Workflow + +### 1. Development + +```bash +# Create/modify dbt models +vim models/new_analysis.sql + +# Run transformations +mxcp dbt run --select new_analysis + +# Test data quality +mxcp dbt test --select new_analysis + +# Create MXCP endpoint +vim tools/new_endpoint.yml +``` + +### 2. Testing + +```bash +# Validate MXCP endpoint +mxcp validate + +# Test endpoint +mxcp test tool new_endpoint +``` + +### 3. Production + +```bash +# Run dbt in production +mxcp dbt run --profile production + +# Start MXCP server +mxcp serve --profile production +``` + +## Advanced Patterns + +### Multi-Source Aggregation + +```sql +-- models/unified_metrics.sql +{{ config(materialized='table') }} + +WITH external_data AS ( + SELECT * FROM read_json('https://api.example.com/metrics') +), +internal_data AS ( + SELECT * FROM {{ source('raw', 'internal_metrics') }} +), +third_party AS ( + SELECT * FROM read_parquet('s3://bucket/data/*.parquet') +) +SELECT * FROM external_data +UNION ALL +SELECT * FROM internal_data +UNION ALL +SELECT * FROM third_party +``` + +### Dynamic Caching Strategy + +```sql +-- models/live_dashboard.sql +{{ config( + materialized='table', + post_hook="PRAGMA optimize" +) }} + +-- Recent data (refresh hourly) +SELECT * FROM read_json('https://api.metrics.com/live') +WHERE timestamp >= current_timestamp - interval '24 hours' + +UNION ALL + +-- Historical data (cached daily) +SELECT * FROM {{ ref('historical_metrics') }} +WHERE timestamp < current_timestamp - interval '24 hours' +``` + +## Best Practices + +1. **Materialization Strategy** + - Use `table` for frequently queried data + - Use `view` for rarely used transformations + - Use `incremental` for large, append-only datasets + +2. **Naming Conventions** + - `stg_` for staging models + - `int_` for intermediate models + - `fct_` for fact tables + - `dim_` for dimension tables + +3. **Data Quality** + - Add tests to all models + - Document columns + - Use sources for raw data + +4. **Performance** + - Materialize frequently used aggregations + - Use incremental for large datasets + - Add indexes where needed + +5. **Version Control** + - Commit dbt models + - Version dbt_project.yml + - Document model changes diff --git a/skills/mxcp-expert/references/debugging-guide.md b/skills/mxcp-expert/references/debugging-guide.md new file mode 100644 index 0000000..fee359b --- /dev/null +++ b/skills/mxcp-expert/references/debugging-guide.md @@ -0,0 +1,576 @@ +# MXCP Debugging Guide + +**Systematic approach to debugging MXCP servers when things don't work.** + +## Debug Mode + +### Enable Debug Logging + +```bash +# Option 1: Environment variable +export MXCP_DEBUG=1 +mxcp serve + +# Option 2: CLI flag +mxcp serve --debug + +# Option 3: For specific commands +mxcp validate --debug +mxcp test --debug +mxcp run tool my_tool --param key=value --debug +``` + +**Debug mode shows**: +- SQL queries being executed +- Parameter values +- Type conversions +- Error stack traces +- Internal MXCP operations + +## Debugging Workflow + +### Step 1: Identify the Layer + +When something fails, determine which layer has the problem: + +``` +User Request + ↓ +MXCP Validation (YAML structure) + ↓ +Parameter Binding (Type conversion) + ↓ +Python Code Execution (if language: python) + ↓ +SQL Execution (if SQL source) + ↓ +Type Validation (Return type check) + ↓ +Response to LLM +``` + +**Run these commands in order**: + +```bash +# 1. Structure validation +mxcp validate +# If fails → YAML structure issue (go to "YAML Errors" section) + +# 2. Test with known inputs +mxcp test +# If fails → Logic or SQL issue (go to "Test Failures" section) + +# 3. Manual execution +mxcp run tool my_tool --param key=value +# If fails → Runtime issue (go to "Runtime Errors" section) + +# 4. Debug mode +mxcp run tool my_tool --param key=value --debug +# See detailed execution logs +``` + +## Common Issues and Solutions + +### YAML Validation Errors + +#### Error: "Invalid YAML syntax" + +```bash +# Check YAML syntax +mxcp validate --debug + +# Common causes: +# 1. Mixed tabs and spaces (use spaces only) +# 2. Incorrect indentation +# 3. Missing quotes around special characters +# 4. Unclosed quotes or brackets +``` + +**Solution**: +```bash +# Use yamllint to check +pip install yamllint +yamllint tools/my_tool.yml + +# Or use online validator +# https://www.yamllint.com/ +``` + +#### Error: "Missing required field: description" + +```yaml +# ❌ WRONG +tool: + name: my_tool + parameters: [] + source: + code: SELECT * FROM table + +# ✅ CORRECT +tool: + name: my_tool + description: "What this tool does" # ← Added + parameters: [] + source: + code: SELECT * FROM table +``` + +#### Error: "Invalid type specification" + +```yaml +# ❌ WRONG +return: + type: "object" # Quoted string + properties: + id: "integer" # Quoted string + +# ✅ CORRECT +return: + type: object # Unquoted + properties: + id: { type: integer } # Proper structure +``` + +### Test Failures + +#### Error: "Expected X, got Y" in test + +```yaml +# Test says: Expected 5 items, got 3 + +# Debug steps: +# 1. Run SQL directly +mxcp query "SELECT * FROM table WHERE condition" + +# 2. Check test data exists +mxcp query "SELECT COUNT(*) FROM table WHERE condition" + +# 3. Verify filter logic +mxcp run tool my_tool --param key=test_value --debug +``` + +**Common causes**: +- Test data not loaded (`dbt seed` not run) +- Wrong filter condition in SQL +- Test expects wrong values + +#### Error: "Type mismatch" + +```yaml +# Test fails: Expected integer, got string + +# Check SQL output types +mxcp query "DESCRIBE table" + +# Fix: Cast in SQL +SELECT + CAST(column AS INTEGER) as column # Explicit cast +FROM table +``` + +### SQL Errors + +#### Error: "Table 'xyz' does not exist" + +```bash +# List all tables +mxcp query "SHOW TABLES" + +# Check if dbt models/seeds loaded +dbt seed +dbt run + +# Verify table name (case-sensitive) +mxcp query "SELECT * FROM xyz LIMIT 1" +``` + +#### Error: "Column 'abc' not found" + +```bash +# Show table schema +mxcp query "DESCRIBE table_name" + +# Check column names (case-sensitive) +mxcp query "SELECT * FROM table_name LIMIT 1" + +# Common issue: typo or wrong case +SELECT customer_id # ← Check exact spelling +``` + +#### Error: "Syntax error near..." + +```bash +# Test SQL directly with debug +mxcp query "YOUR SQL HERE" --debug + +# Common SQL syntax errors: +# 1. Missing quotes around strings +# 2. Wrong parameter binding syntax (use $param not :param) +# 3. DuckDB-specific syntax issues +``` + +### Parameter Binding Errors + +#### Error: "Unbound parameter: $param1" + +```yaml +# ❌ WRONG: Parameter used but not defined +tool: + name: my_tool + parameters: + - name: other_param + type: string + source: + code: SELECT * FROM table WHERE col = $param1 # ← Not defined! + +# ✅ CORRECT: Define all parameters +tool: + name: my_tool + parameters: + - name: param1 # ← Added + type: string + - name: other_param + type: string + source: + code: SELECT * FROM table WHERE col = $param1 +``` + +#### Error: "Type mismatch for parameter" + +```yaml +# MXCP tries to convert "abc" to integer → fails + +# ✅ Solution: Validate types match usage +parameters: + - name: age + type: integer # ← Must be integer for numeric comparison +source: + code: SELECT * FROM users WHERE age > $age # Numeric comparison +``` + +### Python Errors + +#### Error: "ModuleNotFoundError: No module named 'xyz'" + +```bash +# Check requirements.txt exists +cat requirements.txt + +# Install dependencies +pip install -r requirements.txt + +# Or install specific module +pip install xyz +``` + +#### Error: "ImportError: cannot import name 'db'" + +```python +# ❌ WRONG import path +from mxcp import db + +# ✅ CORRECT import path +from mxcp.runtime import db +``` + +#### Error: "Function 'xyz' not found in module" + +```yaml +# tools/my_tool.yml +source: + file: ../python/my_module.py # ← Check file path + +# Common issues: +# 1. Wrong file path (use ../ to go up from tools/) +# 2. Function name typo +# 3. Function not exported (not at module level) +``` + +**Check function exists**: +```bash +# Read Python file +cat python/my_module.py | grep "^def\|^async def" + +# Should see your function listed +``` + +#### Error: "Async function called incorrectly" + +```python +# ❌ WRONG: Calling async function without await +def my_tool(): + result = async_function() # ← Missing await! + return result + +# ✅ CORRECT: Properly handle async +async def my_tool(): + result = await async_function() # ← Added await + return result +``` + +### Return Type Validation Errors + +#### Error: "Expected array, got object" + +```yaml +# SQL returns multiple rows (array) but type says object + +# ❌ WRONG +return: + type: object # ← Wrong! SQL returns array +source: + code: SELECT * FROM table # Returns multiple rows + +# ✅ CORRECT +return: + type: array # ← Matches SQL output + items: + type: object +source: + code: SELECT * FROM table +``` + +#### Error: "Missing required field 'xyz'" + +```yaml +# Return type expects field that SQL doesn't return + +# ❌ WRONG +return: + type: object + properties: + id: { type: integer } + missing_field: { type: string } # ← SQL doesn't return this! +source: + code: SELECT id FROM table # Only returns 'id' + +# ✅ CORRECT: Match return type to actual SQL output +return: + type: object + properties: + id: { type: integer } # Only what SQL returns +source: + code: SELECT id FROM table +``` + +## Debugging Techniques + +### 1. Test SQL Directly + +```bash +# Instead of testing whole tool, test SQL first +mxcp query "SELECT * FROM table WHERE condition LIMIT 5" + +# Test with parameters manually +mxcp query "SELECT * FROM table WHERE id = 123" + +# Check aggregations +mxcp query "SELECT COUNT(*), SUM(amount) FROM table" +``` + +### 2. Add Debug Prints to Python + +```python +# python/my_module.py +import sys + +def my_function(param: str) -> dict: + # Debug output (goes to stderr, won't affect result) + print(f"DEBUG: param={param}", file=sys.stderr) + + result = process(param) + + print(f"DEBUG: result={result}", file=sys.stderr) + + return result +``` + +**View debug output**: +```bash +mxcp serve --debug 2>&1 | grep DEBUG +``` + +### 3. Isolate the Problem + +```python +# Break complex function into steps + +# ❌ Hard to debug +def complex_function(data): + return process(transform(validate(data))) + +# ✅ Easy to debug +def complex_function(data): + print("Step 1: Validate", file=sys.stderr) + validated = validate(data) + + print("Step 2: Transform", file=sys.stderr) + transformed = transform(validated) + + print("Step 3: Process", file=sys.stderr) + processed = process(transformed) + + return processed +``` + +### 4. Test with Minimal Input + +```bash +# Start with simplest possible input +mxcp run tool my_tool --param id=1 + +# Gradually add complexity +mxcp run tool my_tool --param id=1 --param status=active + +# Test edge cases +mxcp run tool my_tool --param id=999999 # Non-existent +mxcp run tool my_tool # Missing required param +``` + +### 5. Check Logs + +```bash +# Server logs (if running) +mxcp serve --debug 2>&1 | tee mxcp.log + +# View recent errors +grep -i error mxcp.log + +# View SQL queries +grep -i select mxcp.log +``` + +### 6. Verify Data + +```bash +# Check seed data loaded +dbt seed --select my_data +mxcp query "SELECT COUNT(*) FROM my_data" + +# Check dbt models built +dbt run --select my_model +mxcp query "SELECT COUNT(*) FROM my_model" + +# Verify test fixtures +mxcp query "SELECT * FROM test_fixtures LIMIT 5" +``` + +## Common Debugging Scenarios + +### Scenario 1: Tool Returns Empty Results + +```bash +# 1. Check if data exists +mxcp query "SELECT COUNT(*) FROM table" +# → If 0, data not loaded (run dbt seed) + +# 2. Check filter condition +mxcp query "SELECT * FROM table WHERE condition" +# → Test condition manually + +# 3. Check parameter value +mxcp run tool my_tool --param key=value --debug +# → See actual SQL with parameter values +``` + +### Scenario 2: Tool Crashes/Returns Error + +```bash +# 1. Validate structure +mxcp validate +# → Fix any YAML errors first + +# 2. Test in isolation +mxcp test tool my_tool +# → See specific error + +# 3. Run with debug +mxcp run tool my_tool --param key=value --debug +# → See full stack trace +``` + +### Scenario 3: Wrong Data Returned + +```bash +# 1. Test SQL directly +mxcp query "SELECT * FROM table LIMIT 5" +# → Verify columns and values + +# 2. Check test assertions +# In YAML, verify test expected results match actual + +# 3. Verify type conversions +mxcp query "SELECT typeof(column) as type FROM table LIMIT 1" +# → Check DuckDB types +``` + +### Scenario 4: Performance Issues + +```bash +# 1. Check query execution time +time mxcp query "SELECT * FROM large_table" + +# 2. Analyze query plan +mxcp query "EXPLAIN SELECT * FROM table WHERE condition" + +# 3. Check for missing indexes +mxcp query "PRAGMA show_tables_expanded" + +# 4. Limit results during development +SELECT * FROM table LIMIT 100 # Add LIMIT for testing +``` + +## Debugging Checklist + +When something doesn't work: + +- [ ] Run `mxcp validate` to check YAML structure +- [ ] Run `mxcp test` to check logic +- [ ] Run `mxcp run tool --debug` to see details +- [ ] Test SQL directly with `mxcp query` +- [ ] Check data loaded with `dbt seed` or `dbt run` +- [ ] Verify Python imports work (`from mxcp.runtime import db`) +- [ ] Check requirements.txt and install dependencies +- [ ] Add debug prints to Python code +- [ ] Test with minimal/simple inputs first +- [ ] Check return types match actual data +- [ ] Review logs for errors + +## Getting Help + +### Information to Provide + +When asking for help or reporting issues: + +1. **Error message** (full text) +2. **Command that failed** (exact command) +3. **Tool YAML** (relevant parts) +4. **Debug output** (`--debug` flag) +5. **Environment** (`mxcp --version`, `python --version`) + +### Self-Help Steps + +Before asking for help: + +1. Read the error message carefully +2. Check this debugging guide +3. Search error message in documentation +4. Test components in isolation +5. Create minimal reproduction case + +## Summary + +**Debugging workflow**: +1. `mxcp validate` → Fix YAML errors +2. `mxcp test` → Fix logic errors +3. `mxcp run --debug` → See detailed execution +4. `mxcp query` → Test SQL directly +5. Add debug prints → Trace Python execution +6. Test in isolation → Identify exact failure point + +**Remember**: +- Start simple, add complexity gradually +- Test each layer independently +- Use debug mode liberally +- Check data loaded before testing queries +- Verify types match at every step diff --git a/skills/mxcp-expert/references/duckdb-essentials.md b/skills/mxcp-expert/references/duckdb-essentials.md new file mode 100644 index 0000000..4a25ba9 --- /dev/null +++ b/skills/mxcp-expert/references/duckdb-essentials.md @@ -0,0 +1,546 @@ +# DuckDB Essentials for MXCP + +Essential DuckDB knowledge for building MXCP servers with embedded analytics. + +## What is DuckDB? + +**DuckDB is an embedded, in-process SQL OLAP database** - think "SQLite for analytics". It runs directly in your MXCP server process without needing a separate database server. + +**Key characteristics**: +- **Embedded**: No server setup, no configuration +- **Fast**: Vectorized execution engine, parallel processing +- **Versatile**: Reads CSV, Parquet, JSON directly from disk or URLs +- **SQL**: Full SQL support with analytical extensions +- **Portable**: Single-file database, easy to move/backup + +**MXCP uses DuckDB by default** for all SQL-based tools and resources. + +## Core Features for MXCP + +### 1. Direct File Reading + +**DuckDB can query files without importing them first**: + +```sql +-- Query CSV directly +SELECT * FROM 'data/sales.csv' + +-- Query with explicit reader +SELECT * FROM read_csv_auto('data/sales.csv') + +-- Query Parquet +SELECT * FROM 'data/sales.parquet' + +-- Query JSON +SELECT * FROM read_json_auto('data/events.json') + +-- Query from URL +SELECT * FROM 'https://example.com/data.csv' +``` + +**Auto-detection**: DuckDB automatically infers: +- Column names from headers +- Data types from values +- CSV delimiters, quotes, etc. + +### 2. CSV Import and Export + +**Import CSV to table**: +```sql +-- Create table from CSV +CREATE TABLE sales AS +SELECT * FROM read_csv_auto('sales.csv') + +-- Or use COPY +COPY sales FROM 'sales.csv' (AUTO_DETECT TRUE) +``` + +**Export to CSV**: +```sql +-- Export query results +COPY (SELECT * FROM sales WHERE region = 'US') +TO 'us_sales.csv' (HEADER, DELIMITER ',') +``` + +**CSV reading options**: +```sql +SELECT * FROM read_csv_auto( + 'data.csv', + header = true, + delim = ',', + quote = '"', + dateformat = '%Y-%m-%d' +) +``` + +### 3. Data Types + +**Common DuckDB types** (important for MXCP type validation): + +```sql +-- Numeric +INTEGER, BIGINT, DECIMAL(10,2), DOUBLE + +-- String +VARCHAR, TEXT + +-- Temporal +DATE, TIME, TIMESTAMP, INTERVAL + +-- Complex +ARRAY, STRUCT, MAP, JSON + +-- Boolean +BOOLEAN +``` + +**Type casting**: +```sql +-- Cast to specific type +SELECT CAST(amount AS DECIMAL(10,2)) FROM sales + +-- Short syntax +SELECT amount::DECIMAL(10,2) FROM sales + +-- Date parsing +SELECT CAST('2025-01-15' AS DATE) +``` + +### 4. SQL Extensions + +**DuckDB adds useful SQL extensions beyond standard SQL**: + +**EXCLUDE clause** (select all except): +```sql +-- Select all columns except sensitive ones +SELECT * EXCLUDE (ssn, salary) FROM employees +``` + +**REPLACE clause** (modify columns in SELECT *): +```sql +-- Replace amount with rounded version +SELECT * REPLACE (ROUND(amount, 2) AS amount) FROM sales +``` + +**List aggregation**: +```sql +-- Aggregate into arrays +SELECT + region, + LIST(product) AS products, + LIST(DISTINCT customer) AS customers +FROM sales +GROUP BY region +``` + +**String aggregation**: +```sql +SELECT + department, + STRING_AGG(employee_name, ', ') AS team_members +FROM employees +GROUP BY department +``` + +### 5. Analytical Functions + +**Window functions**: +```sql +-- Running totals +SELECT + date, + amount, + SUM(amount) OVER (ORDER BY date) AS running_total +FROM sales + +-- Ranking +SELECT + product, + sales, + RANK() OVER (ORDER BY sales DESC) AS rank +FROM product_sales + +-- Partitioned windows +SELECT + region, + product, + sales, + AVG(sales) OVER (PARTITION BY region) AS regional_avg +FROM sales +``` + +**Percentiles and statistics**: +```sql +SELECT + PERCENTILE_CONT(0.5) AS median, + PERCENTILE_CONT(0.95) AS p95, + STDDEV(amount) AS std_dev, + CORR(amount, quantity) AS correlation +FROM sales +``` + +### 6. Date and Time Functions + +```sql +-- Current timestamp +SELECT CURRENT_TIMESTAMP + +-- Date arithmetic +SELECT date + INTERVAL '7 days' AS next_week +SELECT date - INTERVAL '1 month' AS last_month + +-- Date truncation +SELECT DATE_TRUNC('month', timestamp) AS month +SELECT DATE_TRUNC('week', timestamp) AS week + +-- Date parts +SELECT + YEAR(date) AS year, + MONTH(date) AS month, + DAYOFWEEK(date) AS day_of_week +``` + +### 7. JSON Support + +**Parse JSON strings**: +```sql +-- Extract JSON fields +SELECT + json_extract(data, '$.user_id') AS user_id, + json_extract(data, '$.event_type') AS event_type +FROM events + +-- Arrow notation (shorthand) +SELECT + data->'user_id' AS user_id, + data->>'event_type' AS event_type +FROM events +``` + +**Read JSON files**: +```sql +SELECT * FROM read_json_auto('events.json') +``` + +### 8. Performance Features + +**Parallel execution** (automatic): +- DuckDB uses all CPU cores automatically +- No configuration needed + +**Larger-than-memory processing**: +- Spills to disk when needed +- Handles datasets larger than RAM + +**Columnar storage**: +- Efficient for analytical queries +- Fast aggregations and filters + +**Indexes** (for point lookups): +```sql +CREATE INDEX idx_customer ON sales(customer_id) +``` + +## MXCP Integration + +### Database Connection + +**Automatic in MXCP** - no setup needed: +```yaml +# mxcp-site.yml +# DuckDB is the default, no configuration required +``` + +**Environment variable** for custom path: +```bash +# Default database path is data/db-default.duckdb +export MXCP_DUCKDB_PATH="/path/to/data/db-default.duckdb" +mxcp serve +``` + +**Profile-specific databases**: +```yaml +# mxcp-site.yml +profiles: + development: + database: + path: "dev.duckdb" + production: + database: + path: "prod.duckdb" +``` + +### Using DuckDB in MXCP Tools + +**Direct SQL queries**: +```yaml +# tools/query_sales.yml +mxcp: 1 +tool: + name: query_sales + source: + code: | + SELECT + region, + SUM(amount) as total, + COUNT(*) as count + FROM sales + WHERE sale_date >= $start_date + GROUP BY region + ORDER BY total DESC +``` + +**Query CSV files directly**: +```yaml +tool: + name: analyze_upload + source: + code: | + SELECT + COUNT(*) as rows, + COUNT(DISTINCT customer_id) as unique_customers, + SUM(amount) as total_revenue + FROM 'uploads/$filename' +``` + +**Complex analytical queries**: +```yaml +tool: + name: customer_cohorts + source: + code: | + WITH first_purchase AS ( + SELECT + customer_id, + MIN(DATE_TRUNC('month', purchase_date)) AS cohort_month + FROM purchases + GROUP BY customer_id + ), + cohort_size AS ( + SELECT + cohort_month, + COUNT(DISTINCT customer_id) AS cohort_size + FROM first_purchase + GROUP BY cohort_month + ) + SELECT + fp.cohort_month, + DATE_TRUNC('month', p.purchase_date) AS activity_month, + COUNT(DISTINCT p.customer_id) AS active_customers, + cs.cohort_size, + COUNT(DISTINCT p.customer_id)::FLOAT / cs.cohort_size AS retention_rate + FROM purchases p + JOIN first_purchase fp ON p.customer_id = fp.customer_id + JOIN cohort_size cs ON fp.cohort_month = cs.cohort_month + GROUP BY fp.cohort_month, activity_month, cs.cohort_size + ORDER BY fp.cohort_month, activity_month +``` + +### Using DuckDB in Python Endpoints + +**Access via MXCP runtime**: +```python +from mxcp.runtime import db + +def analyze_data(region: str) -> dict: + # Execute query + result = db.execute( + "SELECT SUM(amount) as total FROM sales WHERE region = $1", + {"region": region} + ) + + # Fetch results + row = result.fetchone() + return {"total": row["total"]} + +def batch_insert(records: list[dict]) -> dict: + # Insert data + db.execute( + "INSERT INTO logs (timestamp, event) VALUES ($1, $2)", + [(r["timestamp"], r["event"]) for r in records] + ) + + return {"inserted": len(records)} +``` + +**Read files in Python**: +```python +def import_csv(filepath: str) -> dict: + # Create table from CSV + db.execute(f""" + CREATE TABLE imported_data AS + SELECT * FROM read_csv_auto('{filepath}') + """) + + # Get stats + result = db.execute("SELECT COUNT(*) as count FROM imported_data") + return {"rows_imported": result.fetchone()["count"]} +``` + +## Best Practices for MXCP + +### 1. Use Parameter Binding + +**ALWAYS use parameterized queries** to prevent SQL injection: + +✅ **Correct**: +```yaml +source: + code: | + SELECT * FROM sales WHERE region = $region +``` + +❌ **WRONG** (SQL injection risk): +```yaml +source: + code: | + SELECT * FROM sales WHERE region = '$region' +``` + +### 2. Optimize Queries + +**Index frequently filtered columns**: +```sql +CREATE INDEX idx_customer ON orders(customer_id) +CREATE INDEX idx_date ON orders(order_date) +``` + +**Use EXPLAIN to analyze queries**: +```sql +EXPLAIN SELECT * FROM large_table WHERE id = 123 +``` + +**Materialize complex aggregations** (via dbt models): +```sql +-- Instead of computing on every query +-- Create a materialized view via dbt +CREATE TABLE daily_summary AS +SELECT + DATE_TRUNC('day', timestamp) AS date, + COUNT(*) AS count, + SUM(amount) AS total +FROM transactions +GROUP BY date +``` + +### 3. Handle Large Datasets + +**For large CSVs** (>100MB): +- Use Parquet format instead (much faster) +- Create tables rather than querying files directly +- Use dbt to materialize transformations + +**Conversion to Parquet**: +```sql +COPY (SELECT * FROM 'large_data.csv') +TO 'large_data.parquet' (FORMAT PARQUET) +``` + +### 4. Data Types in MXCP + +**Match DuckDB types to MXCP types**: + +```yaml +# MXCP tool definition +parameters: + - name: amount + type: number # → DuckDB DOUBLE + - name: quantity + type: integer # → DuckDB INTEGER + - name: description + type: string # → DuckDB VARCHAR + - name: created_at + type: string + format: date-time # → DuckDB TIMESTAMP + - name: is_active + type: boolean # → DuckDB BOOLEAN +``` + +### 5. Database File Management + +**Backup**: +```bash +# DuckDB is a single file - just copy it (default: data/db-default.duckdb) +cp data/db-default.duckdb data/db-default.duckdb.backup +``` + +**Export to SQL**: +```sql +EXPORT DATABASE 'backup_directory' +``` + +**Import from SQL**: +```sql +IMPORT DATABASE 'backup_directory' +``` + +## Common Patterns in MXCP + +### Pattern 1: CSV → Table → Query + +```bash +# 1. Load CSV via dbt seed +dbt seed --select customers + +# 2. Query from MXCP tool +SELECT * FROM customers WHERE country = $country +``` + +### Pattern 2: External Data Caching + +```sql +-- dbt model: cache_external_data.sql +{{ config(materialized='table') }} + +SELECT * FROM read_csv_auto('https://example.com/data.csv') +``` + +### Pattern 3: Multi-File Aggregation + +```sql +-- Query multiple CSVs +SELECT * FROM 'data/*.csv' + +-- Union multiple Parquet files +SELECT * FROM 'archive/2025-*.parquet' +``` + +### Pattern 4: Real-time + Historical + +```sql +-- Combine recent API data with historical cache +SELECT * FROM read_json_auto('https://api.com/recent') +UNION ALL +SELECT * FROM historical_data WHERE date < CURRENT_DATE - INTERVAL '7 days' +``` + +## Troubleshooting + +**Issue**: "Table does not exist" +**Solution**: Ensure dbt models/seeds have been run, check table name spelling + +**Issue**: "Type mismatch" +**Solution**: Add explicit CAST() or update schema.yml with correct data types + +**Issue**: "Out of memory" +**Solution**: Reduce query scope, add WHERE filters, materialize intermediate results + +**Issue**: "CSV parsing error" +**Solution**: Use read_csv_auto with explicit options (delim, quote, etc.) + +**Issue**: "Slow queries" +**Solution**: Add indexes, materialize via dbt, use Parquet instead of CSV + +## Summary for MXCP Builders + +When building MXCP servers with DuckDB: + +1. **Use parameterized queries** (`$param`) to prevent injection +2. **Load CSVs via dbt seeds** for version control and validation +3. **Materialize complex queries** as dbt models +4. **Index frequently filtered columns** for performance +5. **Use Parquet for large datasets** (>100MB) +6. **Match MXCP types to DuckDB types** in tool definitions +7. **Leverage DuckDB extensions** (EXCLUDE, REPLACE, window functions) + +DuckDB is the powerhouse behind MXCP's data capabilities - understanding it enables building robust, high-performance MCP servers. diff --git a/skills/mxcp-expert/references/endpoint-patterns.md b/skills/mxcp-expert/references/endpoint-patterns.md new file mode 100644 index 0000000..b50ca8c --- /dev/null +++ b/skills/mxcp-expert/references/endpoint-patterns.md @@ -0,0 +1,187 @@ +# Endpoint Patterns + +Complete examples for creating MXCP endpoints (tools, resources, prompts). + +## SQL Tool - Data Query + +```yaml +# tools/sales_report.yml +mxcp: 1 +tool: + name: sales_report + description: "Get sales data by region and date range" + parameters: + - name: region + type: string + examples: ["US-West"] + - name: start_date + type: string + format: date + - name: end_date + type: string + format: date + return: + type: object + properties: + total_sales: { type: number } + count: { type: integer } + source: + code: | + SELECT SUM(amount) as total_sales, COUNT(*) as count + FROM sales + WHERE region = $region + AND sale_date BETWEEN $start_date AND $end_date +``` + +## Python Tool - ML/API Integration + +```yaml +# tools/analyze_sentiment.yml +mxcp: 1 +tool: + name: analyze_sentiment + description: "Analyze sentiment using ML" + language: python + parameters: + - name: texts + type: array + items: { type: string } + return: + type: array + items: + type: object + properties: + text: { type: string } + sentiment: { type: string } + confidence: { type: number } + source: + file: ../python/sentiment.py +``` + +```python +# python/sentiment.py +from mxcp.runtime import db, on_init +import asyncio + +@on_init +def load_model(): + # Load model once at startup + pass + +async def analyze_sentiment(texts: list[str]) -> list[dict]: + async def analyze_one(text: str) -> dict: + sentiment = "positive" if "good" in text else "neutral" + + db.execute( + "INSERT INTO logs (text, sentiment) VALUES ($1, $2)", + {"text": text, "sentiment": sentiment} + ) + + return {"text": text, "sentiment": sentiment, "confidence": 0.85} + + return await asyncio.gather(*[analyze_one(t) for t in texts]) +``` + +## Resource - Data Access + +```yaml +# resources/customer_data.yml +mxcp: 1 +resource: + uri: "customer://data/{customer_id}" + description: "Get customer profile" + mime_type: "application/json" + parameters: + - name: customer_id + type: string + return: + type: object + properties: + id: { type: string } + name: { type: string } + email: { type: string } + source: + code: | + SELECT id, name, email FROM customers WHERE id = $customer_id +``` + +## Prompt Template + +```yaml +# prompts/customer_analysis.yml +mxcp: 1 +prompt: + name: customer_analysis + description: "Analyze customer behavior" + parameters: + - name: customer_id + type: string + messages: + - role: system + type: text + prompt: "You are a customer analytics expert." + - role: user + type: text + prompt: "Analyze customer {{ customer_id }} and provide insights." +``` + +## Combined SQL + Python + +```yaml +# tools/customer_insights.yml +mxcp: 1 +tool: + name: customer_insights + language: python + source: + file: ../python/insights.py +``` + +```python +# python/insights.py +from mxcp.runtime import db + +def customer_insights(customer_id: str) -> dict: + # SQL for aggregation + stats = db.execute(""" + SELECT COUNT(*) as orders, SUM(amount) as total + FROM orders WHERE customer_id = $id + """, {"id": customer_id}).fetchone() + + # Python for analysis + trend = calculate_trend(stats) + + return {**dict(stats), "trend": trend} +``` + +## With Policies + +```yaml +tool: + name: employee_data + policies: + input: + - condition: "!('hr.read' in user.permissions)" + action: deny + output: + - condition: "user.role != 'hr_manager'" + action: filter_fields + fields: ["salary", "ssn"] +``` + +## With Tests + +```yaml +tool: + name: calculate_total + tests: + - name: "basic_test" + arguments: + - key: amount + value: 100 + - key: tax_rate + value: 0.1 + result: + total: 110 + tax: 10 +``` diff --git a/skills/mxcp-expert/references/error-handling-guide.md b/skills/mxcp-expert/references/error-handling-guide.md new file mode 100644 index 0000000..c313495 --- /dev/null +++ b/skills/mxcp-expert/references/error-handling-guide.md @@ -0,0 +1,635 @@ +# Error Handling Guide + +**Comprehensive error handling for MXCP servers: SQL errors (managed by MXCP) and Python errors (YOU must handle).** + +## Two Types of Error Handling + +### 1. SQL Errors (Managed by MXCP) + +**MXCP automatically handles**: +- SQL syntax errors +- Type mismatches +- Parameter binding errors +- Database connection errors + +**Your responsibility**: +- Write correct SQL +- Use proper parameter binding (`$param`) +- Match return types to actual data + +### 2. Python Errors (YOU Must Handle) + +**You MUST handle**: +- External API failures +- Invalid input +- Resource not found +- Business logic errors +- Async/await errors + +**Return structured error objects, don't raise exceptions to MXCP.** + +## Python Error Handling Pattern + +### ❌ WRONG: Let Exceptions Bubble Up + +```python +# python/api_wrapper.py +async def fetch_user(user_id: int) -> dict: + async with httpx.AsyncClient() as client: + response = await client.get(f"https://api.example.com/users/{user_id}") + response.raise_for_status() # ❌ Will crash if 404/500! + return response.json() +``` + +**Problem**: When API returns 404, exception crashes the tool. LLM gets unhelpful error. + +### ✅ CORRECT: Return Structured Errors + +```python +# python/api_wrapper.py +import httpx + +async def fetch_user(user_id: int) -> dict: + """ + Fetch user from external API. + + Returns: + Success: {"success": true, "user": {...}} + Error: {"success": false, "error": "User not found", "error_code": "NOT_FOUND"} + """ + try: + async with httpx.AsyncClient(timeout=10.0) as client: + response = await client.get( + f"https://api.example.com/users/{user_id}" + ) + + if response.status_code == 404: + return { + "success": False, + "error": f"User with ID {user_id} not found", + "error_code": "NOT_FOUND", + "user_id": user_id + } + + if response.status_code >= 500: + return { + "success": False, + "error": "External API is currently unavailable. Please try again later.", + "error_code": "API_ERROR", + "status_code": response.status_code + } + + response.raise_for_status() # Other HTTP errors + + return { + "success": True, + "user": response.json() + } + + except httpx.TimeoutException: + return { + "success": False, + "error": "Request timed out after 10 seconds. The API may be slow or unavailable.", + "error_code": "TIMEOUT" + } + + except httpx.HTTPError as e: + return { + "success": False, + "error": f"HTTP error occurred: {str(e)}", + "error_code": "HTTP_ERROR" + } + + except Exception as e: + return { + "success": False, + "error": f"Unexpected error: {str(e)}", + "error_code": "UNKNOWN_ERROR" + } +``` + +**Why good**: +- ✅ LLM gets clear error message +- ✅ LLM knows what went wrong (error_code) +- ✅ LLM can take action (retry, try different ID, etc.) +- ✅ Tool never crashes + +## Error Response Structure + +### Standard Error Format + +```python +{ + "success": False, + "error": "Human-readable error message for LLM", + "error_code": "MACHINE_READABLE_CODE", + "details": { # Optional: additional context + "attempted_value": user_id, + "valid_range": "1-1000" + } +} +``` + +### Standard Success Format + +```python +{ + "success": True, + "data": { + # Actual response data + } +} +``` + +## Common Error Scenarios + +### 1. Input Validation Errors + +```python +def process_order(order_id: str, quantity: int) -> dict: + """Process an order with validation""" + + # Validate order_id format + if not order_id.startswith("ORD_"): + return { + "success": False, + "error": f"Invalid order ID format. Expected format: 'ORD_XXXXX', got: '{order_id}'", + "error_code": "INVALID_FORMAT", + "expected_format": "ORD_XXXXX", + "provided": order_id + } + + # Validate quantity range + if quantity <= 0: + return { + "success": False, + "error": f"Quantity must be positive. Got: {quantity}", + "error_code": "INVALID_QUANTITY", + "provided": quantity, + "valid_range": "1 or greater" + } + + if quantity > 1000: + return { + "success": False, + "error": f"Quantity {quantity} exceeds maximum allowed (1000). Please split into multiple orders.", + "error_code": "QUANTITY_EXCEEDED", + "provided": quantity, + "maximum": 1000 + } + + # Process order... + return {"success": True, "order_id": order_id, "quantity": quantity} +``` + +### 2. Resource Not Found Errors + +```python +from mxcp.runtime import db + +def get_customer(customer_id: str) -> dict: + """Get customer by ID with proper error handling""" + + try: + result = db.execute( + "SELECT * FROM customers WHERE customer_id = $1", + {"customer_id": customer_id} + ) + + customer = result.fetchone() + + if customer is None: + return { + "success": False, + "error": f"Customer '{customer_id}' not found in database. Use list_customers to see available customers.", + "error_code": "CUSTOMER_NOT_FOUND", + "customer_id": customer_id, + "suggestion": "Call list_customers tool to see all available customer IDs" + } + + return { + "success": True, + "customer": dict(customer) + } + + except Exception as e: + return { + "success": False, + "error": f"Database error while fetching customer: {str(e)}", + "error_code": "DATABASE_ERROR" + } +``` + +### 3. External API Errors + +```python +import httpx + +async def create_customer_in_stripe(email: str, name: str) -> dict: + """Create Stripe customer with comprehensive error handling""" + + try: + import stripe + from mxcp.runtime import get_secret + + # Get API key + secret = get_secret("stripe") + if not secret: + return { + "success": False, + "error": "Stripe API key not configured. Please set up 'stripe' secret in config.yml", + "error_code": "MISSING_CREDENTIALS", + "required_secret": "stripe" + } + + stripe.api_key = secret.get("api_key") + + # Create customer + customer = stripe.Customer.create( + email=email, + name=name + ) + + return { + "success": True, + "customer_id": customer.id, + "email": customer.email + } + + except stripe.error.InvalidRequestError as e: + return { + "success": False, + "error": f"Invalid request to Stripe: {str(e)}", + "error_code": "INVALID_REQUEST", + "details": str(e) + } + + except stripe.error.AuthenticationError: + return { + "success": False, + "error": "Stripe API key is invalid or expired. Please update credentials.", + "error_code": "AUTHENTICATION_FAILED" + } + + except stripe.error.RateLimitError: + return { + "success": False, + "error": "Stripe rate limit exceeded. Please try again in a few seconds.", + "error_code": "RATE_LIMIT", + "suggestion": "Wait 5-10 seconds and retry" + } + + except stripe.error.StripeError as e: + return { + "success": False, + "error": f"Stripe error: {str(e)}", + "error_code": "STRIPE_ERROR" + } + + except ImportError: + return { + "success": False, + "error": "Stripe library not installed. Run: pip install stripe", + "error_code": "MISSING_DEPENDENCY", + "fix": "pip install stripe>=5.0.0" + } + + except Exception as e: + return { + "success": False, + "error": f"Unexpected error: {str(e)}", + "error_code": "UNKNOWN_ERROR" + } +``` + +### 4. Business Logic Errors + +```python +def transfer_funds(from_account: str, to_account: str, amount: float) -> dict: + """Transfer funds with business logic validation""" + + # Check amount + if amount <= 0: + return { + "success": False, + "error": f"Transfer amount must be positive. Got: ${amount}", + "error_code": "INVALID_AMOUNT" + } + + # Check account exists and get balance + from_balance = db.execute( + "SELECT balance FROM accounts WHERE account_id = $1", + {"account_id": from_account} + ).fetchone() + + if from_balance is None: + return { + "success": False, + "error": f"Source account '{from_account}' not found", + "error_code": "ACCOUNT_NOT_FOUND", + "account_id": from_account + } + + # Check sufficient funds + if from_balance["balance"] < amount: + return { + "success": False, + "error": f"Insufficient funds. Available: ${from_balance['balance']:.2f}, Requested: ${amount:.2f}", + "error_code": "INSUFFICIENT_FUNDS", + "available": from_balance["balance"], + "requested": amount, + "shortfall": amount - from_balance["balance"] + } + + # Perform transfer... + return { + "success": True, + "transfer_id": "TXN_12345", + "from_account": from_account, + "to_account": to_account, + "amount": amount + } +``` + +### 5. Async/Await Errors + +```python +import asyncio + +async def fetch_multiple_users(user_ids: list[int]) -> dict: + """Fetch multiple users concurrently with error handling""" + + async def fetch_one(user_id: int) -> dict: + try: + async with httpx.AsyncClient(timeout=5.0) as client: + response = await client.get(f"https://api.example.com/users/{user_id}") + + if response.status_code == 404: + return { + "user_id": user_id, + "success": False, + "error": f"User {user_id} not found" + } + + response.raise_for_status() + return { + "user_id": user_id, + "success": True, + "user": response.json() + } + + except asyncio.TimeoutError: + return { + "user_id": user_id, + "success": False, + "error": f"Timeout fetching user {user_id}" + } + + except Exception as e: + return { + "user_id": user_id, + "success": False, + "error": str(e) + } + + # Fetch all concurrently + results = await asyncio.gather(*[fetch_one(uid) for uid in user_ids]) + + # Separate successes and failures + successes = [r for r in results if r["success"]] + failures = [r for r in results if not r["success"]] + + return { + "success": len(failures) == 0, + "total_requested": len(user_ids), + "successful": len(successes), + "failed": len(failures), + "users": [r["user"] for r in successes], + "errors": [{"user_id": r["user_id"], "error": r["error"]} for r in failures] + } +``` + +## Error Messages for LLMs + +### Principles for Good Error Messages + +1. **Be Specific**: Tell exactly what went wrong +2. **Be Actionable**: Suggest what to do next +3. **Provide Context**: Include relevant values/IDs +4. **Use Plain Language**: Avoid technical jargon + +### ❌ BAD Error Messages + +```python +return {"error": "Error"} # ❌ Useless +return {"error": "Invalid input"} # ❌ Which input? Why invalid? +return {"error": "DB error"} # ❌ What kind of error? +return {"error": str(e)} # ❌ Raw exception message (often cryptic) +``` + +### ✅ GOOD Error Messages + +```python +return { + "error": "Customer ID 'CUST_999' not found. Use list_customers to see available IDs." +} + +return { + "error": "Date format invalid. Expected 'YYYY-MM-DD' (e.g., '2024-01-15'), got: '01/15/2024'" +} + +return { + "error": "Quantity 5000 exceeds maximum allowed (1000). Split into multiple orders or contact support." +} + +return { + "error": "API rate limit exceeded. Please wait 30 seconds and try again." +} +``` + +## SQL Error Handling (MXCP Managed) + +### You Don't Handle These (MXCP Does) + +MXCP automatically handles and returns errors for: +- Invalid SQL syntax +- Missing tables/columns +- Type mismatches +- Parameter binding errors + +**Your job**: Write correct SQL and let MXCP handle errors. + +### Prevent SQL Errors + +#### 1. Validate Schema + +```yaml +# Always define return types to match SQL output +tool: + name: get_stats + return: + type: object + properties: + total: { type: number } # Matches SQL: SUM(amount) + count: { type: integer } # Matches SQL: COUNT(*) + source: + code: | + SELECT + SUM(amount) as total, + COUNT(*) as count + FROM orders +``` + +#### 2. Handle NULL Values + +```sql +-- BAD: Might return NULL which breaks type system +SELECT amount FROM orders WHERE id = $order_id + +-- GOOD: Handle potential NULL +SELECT COALESCE(amount, 0) as amount +FROM orders +WHERE id = $order_id + +-- GOOD: Use IFNULL/COALESCE for aggregations +SELECT + COALESCE(SUM(amount), 0) as total, + COALESCE(AVG(amount), 0) as average +FROM orders +WHERE status = $status +``` + +#### 3. Handle Empty Results + +```sql +-- If no results, return empty array (not NULL) +SELECT * FROM customers WHERE city = $city +-- Returns: [] if no customers (MXCP handles this) + +-- For aggregations, always return a row +SELECT + COUNT(*) as count, + COALESCE(SUM(amount), 0) as total +FROM orders +WHERE status = $status +-- Always returns one row, even if no matching orders +``` + +## Error Codes Convention + +**Use consistent error codes across your tools**: + +```python +# Standard error codes +ERROR_CODES = { + # Input validation + "INVALID_FORMAT": "Input format is incorrect", + "INVALID_RANGE": "Value outside valid range", + "MISSING_REQUIRED": "Required parameter missing", + + # Resource errors + "NOT_FOUND": "Resource not found", + "ALREADY_EXISTS": "Resource already exists", + "DELETED": "Resource has been deleted", + + # Permission errors + "UNAUTHORIZED": "User not authenticated", + "FORBIDDEN": "User lacks permission", + + # External service errors + "API_ERROR": "External API error", + "TIMEOUT": "Request timed out", + "RATE_LIMIT": "Rate limit exceeded", + + # System errors + "DATABASE_ERROR": "Database operation failed", + "CONFIGURATION_ERROR": "Missing or invalid configuration", + "DEPENDENCY_ERROR": "Required library not installed", + + # Business logic errors + "INSUFFICIENT_FUNDS": "Not enough balance", + "INVALID_STATE": "Operation not allowed in current state", + "QUOTA_EXCEEDED": "Usage quota exceeded", + + # Unknown + "UNKNOWN_ERROR": "Unexpected error occurred" +} +``` + +## Testing Error Handling + +### Unit Tests for Error Cases + +```python +# tests/test_error_handling.py +import pytest +from python.my_module import fetch_user + +@pytest.mark.asyncio +async def test_fetch_user_not_found(httpx_mock): + """Test 404 error handling""" + httpx_mock.add_response( + url="https://api.example.com/users/999", + status_code=404 + ) + + result = await fetch_user(999) + + assert result["success"] is False + assert result["error_code"] == "NOT_FOUND" + assert "999" in result["error"] # Error mentions the ID + +@pytest.mark.asyncio +async def test_fetch_user_timeout(httpx_mock): + """Test timeout handling""" + httpx_mock.add_exception(httpx.TimeoutException("Timeout")) + + result = await fetch_user(123) + + assert result["success"] is False + assert result["error_code"] == "TIMEOUT" + assert "timeout" in result["error"].lower() + +def test_invalid_input(): + """Test input validation""" + result = process_order("INVALID", quantity=5) + + assert result["success"] is False + assert result["error_code"] == "INVALID_FORMAT" + assert "ORD_" in result["error"] # Mentions expected format +``` + +## Error Handling Checklist + +Before declaring Python tool complete: + +- [ ] All external API calls wrapped in try/except +- [ ] All exceptions return structured error objects +- [ ] Error messages are clear and actionable +- [ ] Error codes are consistent +- [ ] Input validation with helpful error messages +- [ ] NULL/None values handled gracefully +- [ ] Timeout handling for network calls +- [ ] Missing dependencies handled (ImportError) +- [ ] Database errors caught and explained +- [ ] Success/failure clearly indicated in response +- [ ] Unit tests for error scenarios +- [ ] Error messages help LLM understand what to do next + +## Summary + +**SQL Tools (MXCP Handles)**: +- Write correct SQL +- Handle NULL values with COALESCE +- Match return types to SQL output + +**Python Tools (YOU Handle)**: +- ✅ Wrap ALL external calls in try/except +- ✅ Return structured error objects (`{"success": False, "error": "...", "error_code": "..."}`) +- ✅ Validate inputs with clear error messages +- ✅ Be specific and actionable in error messages +- ✅ Use consistent error codes +- ✅ Test error scenarios +- ✅ NEVER let exceptions bubble up to MXCP + +**Golden Rule**: Errors should help the LLM understand what went wrong and what to do next. diff --git a/skills/mxcp-expert/references/excel-integration.md b/skills/mxcp-expert/references/excel-integration.md new file mode 100644 index 0000000..e010509 --- /dev/null +++ b/skills/mxcp-expert/references/excel-integration.md @@ -0,0 +1,653 @@ +# Excel File Integration + +Guide for working with Excel files (.xlsx, .xls) in MXCP servers. + +## Overview + +Excel files are common data sources that can be integrated into MXCP servers. DuckDB provides multiple ways to read Excel files, and dbt can be used to manage Excel data as seeds or sources. + +## Reading Excel Files in DuckDB + +### Method 1: Direct Reading with spatial Extension + +DuckDB's spatial extension includes `st_read` which can read Excel files: + +```sql +-- Install and load spatial extension (includes Excel support) +INSTALL spatial; +LOAD spatial; + +-- Read Excel file +SELECT * FROM st_read('data.xlsx'); + +-- Read specific sheet +SELECT * FROM st_read('data.xlsx', layer='Sheet2'); +``` + +### Method 2: Using Python with pandas + +For more control, use Python with pandas: + +```python +# python/excel_reader.py +from mxcp.runtime import db +import pandas as pd + +def load_excel_to_duckdb(filepath: str, table_name: str, sheet_name: str = None) -> dict: + """Load Excel file into DuckDB table""" + # Read Excel with pandas + df = pd.read_excel(filepath, sheet_name=sheet_name) + + # Register DataFrame in DuckDB + db.execute(f"CREATE OR REPLACE TABLE {table_name} AS SELECT * FROM df") + + return { + "table": table_name, + "rows": len(df), + "columns": list(df.columns) + } + +def read_excel_data(filepath: str, sheet_name: str = None) -> list[dict]: + """Read Excel and return as list of dicts""" + df = pd.read_excel(filepath, sheet_name=sheet_name) + return df.to_dict('records') +``` + +### Method 3: Convert to CSV, then use dbt seed + +**Best practice for user-uploaded Excel files**: + +```bash +# Convert Excel to CSV using Python +python -c "import pandas as pd; pd.read_excel('data.xlsx').to_csv('seeds/data.csv', index=False)" + +# Then follow standard dbt seed workflow +cat > seeds/schema.yml < dict: + """Load Excel file into DuckDB for querying""" + if not os.path.exists(filepath): + raise FileNotFoundError(f"Excel file not found: {filepath}") + + # Read Excel + df = pd.read_excel(filepath, sheet_name=sheet_name or 0) + + # Generate table name from filename + table_name = os.path.splitext(os.path.basename(filepath))[0].replace('-', '_').replace(' ', '_') + + # Load into DuckDB + db.execute(f"CREATE OR REPLACE TABLE {table_name} AS SELECT * FROM df") + + return { + "table_name": table_name, + "rows": len(df), + "columns": list(df.columns), + "message": f"Excel loaded. Query with: SELECT * FROM {table_name}" + } +``` + +```yaml +# tools/query_excel_data.yml +mxcp: 1 +tool: + name: query_excel_data + description: "Query data from uploaded Excel file" + parameters: + - name: table_name + type: string + description: "Table name (from upload_excel result)" + - name: filter_column + type: string + required: false + - name: filter_value + type: string + required: false + return: + type: array + source: + code: | + SELECT * FROM {{ table_name }} + WHERE $filter_column IS NULL + OR CAST({{ filter_column }} AS VARCHAR) = $filter_value + LIMIT 1000 +``` + +**Validation workflow for Pattern 1**: +```bash +# 1. Validate MXCP structure +mxcp validate + +# 2. Test upload tool +mxcp test tool upload_excel + +# 3. Manual test with real Excel file +mxcp run tool upload_excel --param filepath="./test.xlsx" + +# 4. Test query tool +mxcp run tool query_excel_data --param table_name="test" + +# 5. All validations must pass before deployment +``` + +### Pattern 2: Excel → dbt Python Model → Analytics + +**User request**: "Process this Excel file with complex formatting and transform the data" + +**RECOMMENDED for complex Excel processing** - Use dbt Python models when: +- Excel has complex formatting or multiple sheets +- Need pandas operations (pivoting, melting, complex string manipulation) +- Data cleaning requires Python logic + +**Implementation**: + +1. **Create dbt Python model** (`models/process_excel.py`): +```python +import pandas as pd + +def model(dbt, session): + # Read Excel file + df = pd.read_excel('data/sales_data.xlsx', sheet_name='Sales') + + # Clean data + df = df.dropna(how='all') # Remove empty rows + df = df.dropna(axis=1, how='all') # Remove empty columns + + # Normalize column names + df.columns = df.columns.str.lower().str.replace(' ', '_') + + # Complex transformations using pandas + df['sale_date'] = pd.to_datetime(df['sale_date']) + df['month'] = df['sale_date'].dt.to_period('M').astype(str) + + # Aggregate + result = df.groupby(['region', 'month']).agg({ + 'amount': 'sum', + 'quantity': 'sum' + }).reset_index() + + return result # Returns DataFrame that becomes a DuckDB table +``` + +2. **Create schema** (`models/schema.yml`): +```yaml +version: 2 + +models: + - name: process_excel + description: "Processed sales data from Excel" + config: + materialized: table + columns: + - name: region + tests: [not_null] + - name: month + tests: [not_null] + - name: amount + tests: [not_null] +``` + +3. **Run the Python model**: +```bash +dbt run --select process_excel +dbt test --select process_excel +``` + +4. **Create MXCP tool**: +```yaml +# tools/sales_analytics.yml +mxcp: 1 +tool: + name: sales_analytics + description: "Get processed sales data from Excel" + parameters: + - name: region + type: string + default: null + return: + type: array + source: + code: | + SELECT * FROM process_excel + WHERE $region IS NULL OR region = $region + ORDER BY month DESC +``` + +5. **Validate**: +```bash +mxcp validate +mxcp test tool sales_analytics +``` + +### Pattern 3: Excel → dbt seed → Analytics + +**User request**: "Analyze this Excel file with aggregations" + +**Use this approach for simpler Excel files** - Convert to CSV first when: +- Excel file is simple with standard formatting +- Want version control for the data (CSV in git) +- Data is static and doesn't change + +**Implementation**: + +1. **Convert Excel to CSV seed**: +```bash +# One-time conversion +python -c " +import pandas as pd +df = pd.read_excel('sales_data.xlsx') +df.to_csv('seeds/sales_data.csv', index=False) +print(f'Converted {len(df)} rows') +" +``` + +2. **Create seed schema**: +```yaml +# seeds/schema.yml +version: 2 + +seeds: + - name: sales_data + description: "Sales data from Excel upload" + columns: + - name: sale_id + tests: [unique, not_null] + - name: sale_date + data_type: date + tests: [not_null] + - name: amount + data_type: decimal + tests: [not_null] + - name: region + tests: [not_null] + - name: product + tests: [not_null] +``` + +3. **Load seed and validate**: +```bash +# Load CSV into DuckDB +dbt seed --select sales_data + +# Run data quality tests +dbt test --select sales_data + +# Verify data loaded correctly +dbt run-operation show_table --args '{"table_name": "sales_data"}' +``` + +**CRITICAL**: Always run `dbt test` after loading seeds to ensure data quality. + +4. **Create analytics model**: +```sql +-- models/sales_analytics.sql +{{ config(materialized='table') }} + +SELECT + region, + product, + DATE_TRUNC('month', sale_date) as month, + COUNT(*) as transaction_count, + SUM(amount) as total_sales, + AVG(amount) as avg_sale, + MIN(amount) as min_sale, + MAX(amount) as max_sale +FROM {{ ref('sales_data') }} +GROUP BY region, product, month +``` + +5. **Create query tool**: +```yaml +# tools/sales_analytics.yml +mxcp: 1 +tool: + name: sales_analytics + description: "Get sales analytics by region and product" + parameters: + - name: region + type: string + required: false + - name: product + type: string + required: false + return: + type: array + source: + code: | + SELECT * FROM sales_analytics + WHERE ($region IS NULL OR region = $region) + AND ($product IS NULL OR product = $product) + ORDER BY month DESC, total_sales DESC +``` + +6. **Validate and test MXCP tool**: +```bash +# Validate MXCP structure +mxcp validate + +# Test tool execution +mxcp test tool sales_analytics + +# Manual verification +mxcp run tool sales_analytics --param region="North" + +# All checks must pass before deployment +``` + +### Pattern 4: Multi-Sheet Excel Processing + +**User request**: "My Excel has multiple sheets, process them all" + +```python +# python/multi_sheet_loader.py +from mxcp.runtime import db +import pandas as pd + +def load_all_sheets(filepath: str) -> dict: + """Load all sheets from Excel file as separate tables""" + # Read all sheets + excel_file = pd.ExcelFile(filepath) + + results = {} + for sheet_name in excel_file.sheet_names: + df = pd.read_excel(filepath, sheet_name=sheet_name) + + # Clean table name + table_name = sheet_name.lower().replace(' ', '_').replace('-', '_') + + # Load to DuckDB + db.execute(f"CREATE OR REPLACE TABLE {table_name} AS SELECT * FROM df") + + results[sheet_name] = { + "table_name": table_name, + "rows": len(df), + "columns": list(df.columns) + } + + return { + "sheets_loaded": len(results), + "sheets": results + } +``` + +## Excel-Specific Considerations + +### Data Type Inference + +Excel doesn't have strict types. Handle type ambiguity: + +```python +def clean_excel_types(df: pd.DataFrame) -> pd.DataFrame: + """Clean common Excel type issues""" + for col in df.columns: + # Convert Excel dates properly + if df[col].dtype == 'object': + try: + df[col] = pd.to_datetime(df[col]) + except: + pass + + # Strip whitespace from strings + if df[col].dtype == 'object': + df[col] = df[col].str.strip() + + return df +``` + +### Handling Headers + +Excel files may have inconsistent headers: + +```python +def normalize_headers(df: pd.DataFrame) -> pd.DataFrame: + """Normalize Excel column names""" + df.columns = ( + df.columns + .str.lower() + .str.replace(' ', '_') + .str.replace('-', '_') + .str.replace('[^a-z0-9_]', '', regex=True) + ) + return df +``` + +### Empty Rows/Columns + +Excel often has empty rows: + +```python +def clean_excel_data(filepath: str, sheet_name: str = None) -> pd.DataFrame: + """Read and clean Excel data""" + df = pd.read_excel(filepath, sheet_name=sheet_name) + + # Remove completely empty rows + df = df.dropna(how='all') + + # Remove completely empty columns + df = df.dropna(axis=1, how='all') + + # Normalize headers + df = normalize_headers(df) + + # Clean types + df = clean_excel_types(df) + + return df +``` + +## Complete Example: Excel Analytics Server + +**Scenario**: User uploads Excel file, wants to query and get statistics + +```bash +# Project structure +excel-analytics/ +├── mxcp-site.yml +├── python/ +│ ├── excel_loader.py +│ └── excel_analytics.py +├── tools/ +│ ├── load_excel.yml +│ ├── query_data.yml +│ └── get_statistics.yml +└── seeds/ + └── schema.yml (if using dbt seed approach) +``` + +**Implementation**: + +```python +# python/excel_loader.py +from mxcp.runtime import db +import pandas as pd +import os + +def normalize_headers(df: pd.DataFrame) -> pd.DataFrame: + df.columns = df.columns.str.lower().str.replace(' ', '_').str.replace('[^a-z0-9_]', '', regex=True) + return df + +def load_excel(filepath: str, sheet_name: str = None) -> dict: + """Load Excel file with cleaning""" + if not os.path.exists(filepath): + raise FileNotFoundError(f"File not found: {filepath}") + + # Read and clean + df = pd.read_excel(filepath, sheet_name=sheet_name or 0) + df = df.dropna(how='all').dropna(axis=1, how='all') + df = normalize_headers(df) + + # Table name from filename + table_name = os.path.splitext(os.path.basename(filepath))[0] + table_name = table_name.lower().replace('-', '_').replace(' ', '_') + + # Load to DuckDB + db.execute(f"CREATE OR REPLACE TABLE {table_name} AS SELECT * FROM df") + + # Get column info + col_info = db.execute(f"DESCRIBE {table_name}").fetchall() + + return { + "table_name": table_name, + "rows": len(df), + "columns": [{"name": c["column_name"], "type": c["column_type"]} for c in col_info] + } + +def get_statistics(table_name: str, numeric_columns: list[str] = None) -> dict: + """Calculate statistics for numeric columns""" + # Get numeric columns if not specified + if not numeric_columns: + schema = db.execute(f"DESCRIBE {table_name}").fetchall() + numeric_columns = [ + c["column_name"] for c in schema + if c["column_type"] in ('INTEGER', 'BIGINT', 'DOUBLE', 'DECIMAL', 'FLOAT') + ] + + if not numeric_columns: + return {"error": "No numeric columns found"} + + # Build statistics query + stats_parts = [] + for col in numeric_columns: + stats_parts.append(f""" + '{col}' as column, + COUNT({col}) as count, + AVG({col}) as mean, + STDDEV({col}) as std_dev, + MIN({col}) as min, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY {col}) as q25, + PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY {col}) as median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY {col}) as q75, + MAX({col}) as max + """) + + query = f""" + SELECT * FROM ( + {' UNION ALL '.join(f'SELECT {part} FROM {table_name}' for part in stats_parts)} + ) + """ + + results = db.execute(query).fetchall() + return {"statistics": results} +``` + +```yaml +# tools/load_excel.yml +mxcp: 1 +tool: + name: load_excel + description: "Load Excel file for querying and analysis" + language: python + parameters: + - name: filepath + type: string + description: "Path to Excel file" + - name: sheet_name + type: string + required: false + return: + type: object + source: + file: ../python/excel_loader.py + tests: + - name: "load_test_file" + arguments: + - key: filepath + value: "test_data.xlsx" + result: + rows: 100 +``` + +## Dependencies + +Add to `requirements.txt`: +``` +openpyxl>=3.0.0 # For .xlsx files +xlrd>=2.0.0 # For .xls files (optional) +pandas>=2.0.0 # For Excel processing +``` + +## Best Practices + +1. **Always clean Excel data**: Remove empty rows/columns, normalize headers +2. **Type validation**: Excel types are unreliable, validate and cast +3. **Use dbt seeds for static data**: Convert Excel → CSV → seed for version control +4. **Use Python for dynamic uploads**: For user-uploaded files during runtime +5. **Document expected format**: Tell users what Excel structure is expected +6. **Error handling**: Excel files can be malformed, handle errors gracefully +7. **Sheet validation**: Check sheet names exist before processing +8. **Memory considerations**: Large Excel files can be slow, consider pagination + +## Troubleshooting + +**Issue**: "No module named 'openpyxl'" +**Solution**: `pip install openpyxl` + +**Issue**: "Excel file empty after loading" +**Solution**: Check for empty rows/columns, use `dropna()` + +**Issue**: "Column names have special characters" +**Solution**: Use `normalize_headers()` function + +**Issue**: "Date columns appear as numbers" +**Solution**: Use `pd.to_datetime()` to convert Excel serial dates + +**Issue**: "Out of memory with large Excel files" +**Solution**: Convert to CSV first, use dbt seed, or process in chunks + +## Summary + +For Excel integration in MXCP: + +1. **User uploads** → Python tool with pandas → DuckDB table → Query tools +2. **Static data** → Convert to CSV → dbt seed → Schema validation → Query tools +3. **Multi-sheet** → Load all sheets as separate tables +4. **Always validate** → Clean headers, types, empty rows +5. **Add statistics tools** → Provide insights on numeric columns diff --git a/skills/mxcp-expert/references/llm-friendly-documentation.md b/skills/mxcp-expert/references/llm-friendly-documentation.md new file mode 100644 index 0000000..3368d13 --- /dev/null +++ b/skills/mxcp-expert/references/llm-friendly-documentation.md @@ -0,0 +1,691 @@ +# LLM-Friendly Documentation Guide + +**CRITICAL: Tools must be self-documenting for LLMs without any prior context.** + +## Core Principle + +**LLMs connecting to MXCP servers have ZERO context about your domain, data, or tools.** + +They only see: +- Tool name +- Tool description +- Parameter names and types +- Parameter descriptions +- Return type structure + +**The documentation YOU provide is the ONLY information they have.** + +## Tool Description Requirements + +### ❌ BAD Tool Description + +```yaml +tool: + name: get_data + description: "Gets data" # ❌ Useless - what data? how? when to use? + parameters: + - name: id + type: string # ❌ No description - what kind of ID? + return: + type: array # ❌ Array of what? +``` + +**Why bad**: LLM has no idea when to use this, what ID means, what data is returned. + +### ✅ GOOD Tool Description + +```yaml +tool: + name: get_customer_orders + description: "Retrieve all orders for a specific customer by customer ID. Returns order history including order date, total amount, status, and items. Use this to answer questions about a customer's purchase history or order status." + parameters: + - name: customer_id + type: string + description: "Unique customer identifier (e.g., 'CUST_12345'). Found in customer records or from list_customers tool." + required: true + examples: ["CUST_12345", "CUST_98765"] + - name: status + type: string + description: "Optional filter by order status. Valid values: 'pending', 'shipped', 'delivered', 'cancelled'. Omit to get all orders." + required: false + examples: ["pending", "shipped"] + return: + type: array + items: + type: object + properties: + order_id: { type: string, description: "Unique order identifier" } + order_date: { type: string, description: "ISO 8601 date when order was placed" } + total_amount: { type: number, description: "Total order value in USD" } + status: { type: string, description: "Current order status" } + items: { type: array, description: "List of items in the order" } +``` + +**Why good**: +- LLM knows WHEN to use it (customer purchase history, order status) +- LLM knows WHAT parameters mean and valid values +- LLM knows WHAT will be returned +- LLM can chain with other tools (mentions list_customers) + +## Description Template + +### Tool-Level Description + +**Format**: ` ` + +```yaml +description: "Retrieve sales analytics by region and time period. Returns aggregated metrics including total sales, transaction count, and average order value. Use this to answer questions about sales performance, regional comparisons, or time-based trends." +``` + +**Must include**: +1. **What**: What data/operation +2. **Returns**: Summary of return data +3. **When**: Use cases / when LLM should call this + +### Parameter Description + +**Format**: ` ` + +```yaml +parameters: + - name: region + type: string + description: "Geographic region code. Valid values: 'north', 'south', 'east', 'west'. Use 'all' for aggregated data across all regions." + examples: ["north", "south", "all"] + + - name: start_date + type: string + format: date + description: "Start date for analytics period in YYYY-MM-DD format. Defaults to 30 days ago if omitted." + required: false + examples: ["2024-01-01", "2024-06-15"] + + - name: limit + type: integer + description: "Maximum number of results to return. Defaults to 100. Set to -1 for all results (use cautiously for large datasets)." + default: 100 + examples: [10, 50, 100] +``` + +**Must include**: +1. **What it is**: Clear explanation +2. **Valid values**: Enums, formats, ranges +3. **Defaults**: If parameter is optional +4. **Examples**: Concrete examples + +### Return Type Description + +**Include descriptions for ALL fields**: + +```yaml +return: + type: object + properties: + total_sales: + type: number + description: "Sum of all sales in USD for the period" + transaction_count: + type: integer + description: "Number of individual transactions" + avg_order_value: + type: number + description: "Average transaction amount (total_sales / transaction_count)" + top_products: + type: array + description: "Top 5 products by revenue" + items: + type: object + properties: + product_id: { type: string, description: "Product identifier" } + product_name: { type: string, description: "Human-readable product name" } + revenue: { type: number, description: "Total revenue for this product in USD" } +``` + +## Combining Tools - Cross-References + +**Help LLMs chain tools together** by mentioning related tools: + +```yaml +tool: + name: get_customer_details + description: "Get detailed information for a specific customer. Use customer_id from list_customers tool or search_customers tool. Returns personal info, account status, and lifetime value." + # ... parameters ... +``` + +```yaml +tool: + name: list_customers + description: "List all customers with optional filtering. Returns customer_id needed for get_customer_details and get_customer_orders tools." + # ... parameters ... +``` + +**LLM workflow enabled**: +1. LLM sees: "I need customer details" +2. Reads: "Use customer_id from list_customers tool" +3. Calls: `list_customers` first +4. Gets: `customer_id` +5. Calls: `get_customer_details` with that ID + +## Examples in Descriptions + +**ALWAYS provide concrete examples**: + +```yaml +parameters: + - name: date_range + type: string + description: "Date range in format 'YYYY-MM-DD to YYYY-MM-DD' or use shortcuts: 'today', 'yesterday', 'last_7_days', 'last_30_days', 'last_month', 'this_year'" + examples: + - "2024-01-01 to 2024-12-31" + - "last_7_days" + - "this_year" +``` + +## Error Cases in Descriptions + +**Document expected errors**: + +```yaml +tool: + name: get_order + description: "Retrieve order by order ID. Returns order details if found. Returns error if order_id doesn't exist or user doesn't have permission to view this order." + parameters: + - name: order_id + type: string + description: "Order identifier. Format: ORD_XXXXXX (e.g., 'ORD_123456'). Returns error if order not found." +``` + +## Resource URIs + +**Make URI templates clear**: + +```yaml +resource: + uri: "customer://profile/{customer_id}" + description: "Access customer profile data. Replace {customer_id} with actual customer ID (e.g., 'CUST_12345'). Returns 404 if customer doesn't exist." + parameters: + - name: customer_id + type: string + description: "Customer identifier from list_customers or search_customers" +``` + +## Prompt Templates + +**Explain template variables clearly**: + +```yaml +prompt: + name: analyze_customer + description: "Generate customer analysis report. Provide customer_id to analyze spending patterns, order frequency, and recommendations." + parameters: + - name: customer_id + type: string + description: "Customer to analyze (from list_customers)" + - name: analysis_type + type: string + description: "Type of analysis: 'spending' (purchase patterns), 'behavior' (order frequency), 'recommendations' (product suggestions)" + examples: ["spending", "behavior", "recommendations"] + messages: + - role: system + type: text + prompt: "You are a customer analytics expert. Analyze data thoroughly and provide actionable insights." + - role: user + type: text + prompt: "Analyze customer {{ customer_id }} focusing on {{ analysis_type }}. Include specific metrics and recommendations." +``` + +## Complete Example: Well-Documented Tool Set + +```yaml +# tools/list_products.yml +mxcp: 1 +tool: + name: list_products + description: "List all available products with optional category filtering. Returns product catalog with IDs, names, prices, and stock levels. Use this to browse products or find product_id for get_product_details tool." + parameters: + - name: category + type: string + description: "Filter by product category. Valid values: 'electronics', 'clothing', 'food', 'books', 'home'. Omit to see all categories." + required: false + examples: ["electronics", "clothing"] + - name: in_stock_only + type: boolean + description: "If true, only return products currently in stock. Default: false (shows all products)." + default: false + return: + type: array + description: "Array of product objects sorted by name" + items: + type: object + properties: + product_id: + type: string + description: "Unique product identifier (use with get_product_details)" + name: + type: string + description: "Product name" + category: + type: string + description: "Product category" + price: + type: number + description: "Current price in USD" + stock: + type: integer + description: "Current stock level (0 = out of stock)" + source: + code: | + SELECT + product_id, + name, + category, + price, + stock + FROM products + WHERE ($category IS NULL OR category = $category) + AND ($in_stock_only = false OR stock > 0) + ORDER BY name +``` + +```yaml +# tools/get_product_details.yml +mxcp: 1 +tool: + name: get_product_details + description: "Get detailed information for a specific product including full description, specifications, reviews, and related products. Use product_id from list_products tool." + parameters: + - name: product_id + type: string + description: "Product identifier from list_products (e.g., 'PROD_12345')" + required: true + examples: ["PROD_12345"] + return: + type: object + description: "Complete product information" + properties: + product_id: { type: string, description: "Product identifier" } + name: { type: string, description: "Product name" } + description: { type: string, description: "Detailed product description" } + price: { type: number, description: "Current price in USD" } + stock: { type: integer, description: "Available quantity" } + specifications: { type: object, description: "Product specs (varies by category)" } + avg_rating: { type: number, description: "Average customer rating (0-5)" } + review_count: { type: integer, description: "Number of customer reviews" } + related_products: { type: array, description: "Product IDs of related items" } + source: + code: | + SELECT * FROM product_details WHERE product_id = $product_id +``` + +## Documentation Quality Checklist + +Before declaring a tool complete, verify: + +### Tool Level: +- [ ] Description explains WHAT it does +- [ ] Description explains WHAT it returns +- [ ] Description explains WHEN to use it +- [ ] Cross-references to related tools (if applicable) +- [ ] Use cases are clear + +### Parameter Level: +- [ ] Every parameter has a description +- [ ] Valid values/formats are documented +- [ ] Examples provided for complex parameters +- [ ] Required vs optional is clear +- [ ] Defaults documented (if optional) + +### Return Type Level: +- [ ] Return type structure is documented +- [ ] Every field has a description +- [ ] Complex nested objects are explained +- [ ] Array item types are described + +### Overall: +- [ ] An LLM reading this can use the tool WITHOUT human explanation +- [ ] An LLM knows WHEN to call this vs other tools +- [ ] An LLM knows HOW to get required parameters +- [ ] An LLM knows WHAT to expect in the response + +## Common Documentation Mistakes + +### ❌ MISTAKE 1: Vague Descriptions +```yaml +description: "Gets user info" # ❌ Which user? What info? When? +``` +✅ **FIX**: +```yaml +description: "Retrieve complete user profile including contact information, account status, and preferences for a specific user. Use user_id from list_users or search_users tools." +``` + +### ❌ MISTAKE 2: Missing Parameter Details +```yaml +parameters: + - name: status + type: string # ❌ What are valid values? +``` +✅ **FIX**: +```yaml +parameters: + - name: status + type: string + description: "Order status filter. Valid values: 'pending', 'processing', 'shipped', 'delivered', 'cancelled'" + examples: ["pending", "shipped"] +``` + +### ❌ MISTAKE 3: Undocumented Return Fields +```yaml +return: + type: object + properties: + total: { type: number } # ❌ Total what? In what units? +``` +✅ **FIX**: +```yaml +return: + type: object + properties: + total: { type: number, description: "Total order amount in USD including tax and shipping" } +``` + +### ❌ MISTAKE 4: No Cross-References +```yaml +tool: + name: get_order_details + parameters: + - name: order_id + type: string # ❌ Where does LLM get this? +``` +✅ **FIX**: +```yaml +tool: + name: get_order_details + description: "Get detailed order information. Use order_id from list_orders or search_orders tools." + parameters: + - name: order_id + type: string + description: "Order identifier (format: ORD_XXXXXX) from list_orders or search_orders" +``` + +### ❌ MISTAKE 5: Technical Jargon Without Explanation +```yaml +description: "Executes SOQL query on SF objects" # ❌ LLM doesn't know SOQL or SF +``` +✅ **FIX**: +```yaml +description: "Query Salesforce data using filters. Searches across accounts, contacts, and opportunities. Returns matching records with standard fields." +``` + +## Testing Documentation Quality + +**Ask yourself**: "If I gave this to an LLM with ZERO context about my domain, could it use this tool correctly?" + +**Test by asking**: +1. When should this tool be called? +2. What parameters are needed and where do I get them? +3. What will I get back? +4. How does this relate to other tools? + +If you can't answer clearly from the YAML alone, **the documentation is insufficient.** + +## Response Format Best Practices + +**Design tool outputs to optimize LLM context usage.** + +### Provide Detail Level Options + +Allow LLMs to request different levels of detail based on their needs. + +```yaml +tool: + name: search_products + parameters: + - name: query + type: string + description: "Product search query" + - name: detail_level + type: string + description: "Level of detail in response" + enum: ["minimal", "standard", "full"] + default: "standard" + examples: + - "minimal: Only ID, name, price (fastest, least context)" + - "standard: Basic info + category + stock" + - "full: All fields including descriptions and specifications" +``` + +**Implementation in SQL**: +```sql +SELECT + CASE $detail_level + WHEN 'minimal' THEN json_object('id', id, 'name', name, 'price', price) + WHEN 'standard' THEN json_object('id', id, 'name', name, 'price', price, 'category', category, 'in_stock', stock > 0) + ELSE json_object('id', id, 'name', name, 'price', price, 'category', category, 'stock', stock, 'description', description, 'specs', specs) + END as product +FROM products +WHERE name LIKE '%' || $query || '%' +``` + +### Use Human-Readable Formats + +**Return data in formats LLMs can easily understand and communicate to users.** + +#### ✅ Good: Human-Readable +```yaml +return: + type: object + properties: + customer_id: { type: string, description: "Customer ID (CUST_12345)" } + customer_name: { type: string, description: "Display name" } + last_order_date: { type: string, description: "Date in YYYY-MM-DD format" } + total_spent: { type: number, description: "Total amount in USD" } + status: { type: string, description: "Account status: active, inactive, suspended" } +``` + +**SQL implementation**: +```sql +SELECT + customer_id, + name as customer_name, + DATE_FORMAT(last_order_date, '%Y-%m-%d') as last_order_date, -- Not epoch timestamp + ROUND(total_spent, 2) as total_spent, + status +FROM customers +``` + +#### ❌ Bad: Opaque/Technical +```yaml +return: + type: object + properties: + cust_id: { type: integer } # Unclear name + ts: { type: integer } # Epoch timestamp - not human readable + amt: { type: number } # Unclear abbreviation + stat_cd: { type: integer } # Status code instead of name +``` + +### Include Display Names with IDs + +When returning IDs, also return human-readable names. + +```yaml +return: + type: object + properties: + assigned_to_user_id: { type: string, description: "User ID" } + assigned_to_name: { type: string, description: "User display name" } + category_id: { type: string, description: "Category ID" } + category_name: { type: string, description: "Category name" } +``` + +**Why**: LLM can understand relationships without additional tool calls. + +### Limit Response Size + +**Prevent overwhelming LLMs with too much data.** + +```yaml +tool: + name: list_transactions + parameters: + - name: limit + type: integer + description: "Maximum number of transactions to return (1-1000)" + default: 100 + minimum: 1 + maximum: 1000 +``` + +**Python implementation with truncation**: +```python +def list_transactions(limit: int = 100) -> dict: + """List recent transactions with size limits""" + + if limit > 1000: + return { + "success": False, + "error": f"Limit of {limit} exceeds maximum (1000). Use date filters to narrow results.", + "error_code": "LIMIT_EXCEEDED", + "suggestion": "Try adding 'start_date' and 'end_date' parameters" + } + + results = db.execute( + "SELECT * FROM transactions ORDER BY date DESC LIMIT $limit", + {"limit": limit} + ) + + return { + "success": True, + "count": len(results), + "limit": limit, + "has_more": len(results) == limit, + "transactions": results, + "note": "Use pagination or filters if more results needed" + } +``` + +### Provide Pagination Metadata + +**Help LLMs understand when more data is available.** + +```yaml +return: + type: object + properties: + items: { type: array, description: "Results for this page" } + total_count: { type: integer, description: "Total matching results" } + returned_count: { type: integer, description: "Number returned in this response" } + has_more: { type: boolean, description: "Whether more results are available" } + next_offset: { type: integer, description: "Offset for next page" } +``` + +**SQL implementation**: +```sql +-- Get total count +WITH total AS ( + SELECT COUNT(*) as count FROM products WHERE category = $category +) +SELECT + json_object( + 'items', (SELECT json_group_array(json_object('id', id, 'name', name)) + FROM products WHERE category = $category LIMIT $limit OFFSET $offset), + 'total_count', (SELECT count FROM total), + 'returned_count', MIN($limit, (SELECT count FROM total) - $offset), + 'has_more', (SELECT count FROM total) > ($offset + $limit), + 'next_offset', $offset + $limit + ) as result +``` + +### Format for Readability + +**Use clear field names and consistent structures.** + +#### ✅ Good: Clear Structure +```yaml +return: + type: object + properties: + summary: + type: object + description: "High-level summary" + properties: + total_orders: { type: integer } + total_revenue: { type: number } + average_order_value: { type: number } + top_products: + type: array + description: "Top 5 selling products" + items: + type: object + properties: + product_name: { type: string } + units_sold: { type: integer } + revenue: { type: number } +``` + +#### ❌ Bad: Flat Unstructured +```yaml +return: + type: object + properties: + total_orders: { type: integer } + total_revenue: { type: number } + product1_name: { type: string } + product1_units: { type: integer } + product2_name: { type: string } + # ...repeated pattern +``` + +### Omit Verbose Metadata + +**Don't return internal/technical metadata that doesn't help LLMs.** + +```yaml +# ✅ GOOD: Essential information only +return: + type: object + properties: + user_id: { type: string } + name: { type: string } + email: { type: string } + profile_image: { type: string, description: "Profile image URL" } + +# ❌ BAD: Too much metadata +return: + type: object + properties: + user_id: { type: string } + name: { type: string } + email: { type: string } + profile_image_small: { type: string } + profile_image_medium: { type: string } + profile_image_large: { type: string } + profile_image_xlarge: { type: string } + internal_db_id: { type: integer } + created_timestamp_unix: { type: integer } + modified_timestamp_unix: { type: integer } + schema_version: { type: integer } +``` + +**Principle**: Include one best representation, not all variations. + +## Summary + +**Every tool must be self-documenting**: +- ✅ Clear, detailed descriptions +- ✅ Documented parameters with examples +- ✅ Documented return types +- ✅ Cross-references to related tools +- ✅ Valid values and formats +- ✅ Use cases explained + +**Response format best practices**: +- ✅ Provide detail level options (minimal/standard/full) +- ✅ Use human-readable formats (dates, names, not codes) +- ✅ Include display names alongside IDs +- ✅ Limit response sizes with clear guidance +- ✅ Provide pagination metadata +- ✅ Structure data clearly +- ✅ Omit verbose internal metadata + +**Remember**: The LLM has NO prior knowledge. Your descriptions are its ONLY guide. diff --git a/skills/mxcp-expert/references/minimal-working-examples.md b/skills/mxcp-expert/references/minimal-working-examples.md new file mode 100644 index 0000000..20f70fe --- /dev/null +++ b/skills/mxcp-expert/references/minimal-working-examples.md @@ -0,0 +1,1122 @@ +# Minimal Working Examples + +**Guaranteed-to-work examples for common MXCP patterns. Copy, test, then customize.** + +## Example 1: CSV File to Queryable Tool + +**Use case**: User has a CSV file, wants to query it. + +**This example is TESTED and WORKS.** + +### Setup + +```bash +# 1. Create project +mkdir csv-query-example && cd csv-query-example +mxcp init --bootstrap + +# 2. Create test CSV +cat > seeds/customers.csv <<'EOF' +customer_id,name,email,city,signup_date +1,John Doe,john@example.com,New York,2024-01-15 +2,Jane Smith,jane@example.com,Los Angeles,2024-02-20 +3,Bob Johnson,bob@example.com,Chicago,2024-03-10 +EOF + +# 3. Create schema +cat > seeds/schema.yml <<'EOF' +version: 2 + +seeds: + - name: customers + description: "Customer master data" + columns: + - name: customer_id + data_type: integer + tests: + - unique + - not_null + - name: name + data_type: varchar + tests: + - not_null + - name: email + data_type: varchar + tests: + - not_null + - name: city + data_type: varchar + - name: signup_date + data_type: date +EOF + +# 4. Load data +dbt seed +dbt test + +# 5. Create query tool +cat > tools/get_customers.yml <<'EOF' +mxcp: 1 +tool: + name: get_customers + description: "Query customers by city or get all customers" + parameters: + - name: city + type: string + description: "Filter by city (optional)" + default: null + return: + type: array + items: + type: object + properties: + customer_id: { type: integer } + name: { type: string } + email: { type: string } + city: { type: string } + signup_date: { type: string } + source: + code: | + SELECT + customer_id, + name, + email, + city, + signup_date::VARCHAR as signup_date + FROM customers + WHERE $city IS NULL OR city = $city + ORDER BY customer_id + tests: + - name: "get_all" + arguments: [] + result: + - customer_id: 1 + - customer_id: 2 + - customer_id: 3 + - name: "filter_by_city" + arguments: + - key: city + value: "Chicago" + result: + - customer_id: 3 + name: "Bob Johnson" +EOF + +# 6. Validate and test +mxcp validate +mxcp test + +# 7. Manual test +mxcp run tool get_customers +mxcp run tool get_customers --param city="New York" + +# 8. Start server +mxcp serve +``` + +**Expected result**: All commands succeed, server starts without errors. + +--- + +## Example 2: Python Tool with API Call + +**Use case**: Wrap an HTTP API as an MCP tool. + +**This example is TESTED and WORKS.** + +### Setup + +```bash +# 1. Create project +mkdir api-wrapper-example && cd api-wrapper-example +mxcp init --bootstrap + +# 2. Create requirements.txt +cat > requirements.txt <<'EOF' +httpx>=0.24.0 +EOF + +# 3. Install dependencies +pip install -r requirements.txt + +# 4. Create Python wrapper +mkdir -p python +cat > python/api_wrapper.py <<'EOF' +import httpx + +async def fetch_users(limit: int = 10) -> dict: + """Fetch users from JSONPlaceholder API""" + try: + async with httpx.AsyncClient(timeout=10.0) as client: + response = await client.get( + "https://jsonplaceholder.typicode.com/users" + ) + response.raise_for_status() + users = response.json() + + # Limit results + limited_users = users[:limit] + + return { + "count": len(limited_users), + "users": [ + { + "id": user["id"], + "name": user["name"], + "email": user["email"], + "city": user["address"]["city"] + } + for user in limited_users + ] + } + except httpx.HTTPError as e: + return {"error": str(e), "users": []} + except Exception as e: + return {"error": f"Unexpected error: {str(e)}", "users": []} + +async def fetch_user(user_id: int) -> dict: + """Fetch single user by ID""" + try: + async with httpx.AsyncClient(timeout=10.0) as client: + response = await client.get( + f"https://jsonplaceholder.typicode.com/users/{user_id}" + ) + response.raise_for_status() + user = response.json() + + return { + "id": user["id"], + "name": user["name"], + "email": user["email"], + "city": user["address"]["city"], + "company": user["company"]["name"] + } + except httpx.HTTPError as e: + return {"error": str(e)} + except Exception as e: + return {"error": f"Unexpected error: {str(e)}"} +EOF + +# 5. Create tools +cat > tools/list_users.yml <<'EOF' +mxcp: 1 +tool: + name: list_users + description: "Get list of users from API" + language: python + parameters: + - name: limit + type: integer + default: 10 + description: "Maximum number of users to return" + return: + type: object + properties: + count: { type: integer } + users: { type: array } + source: + file: ../python/api_wrapper.py + tests: + - name: "default_limit" + arguments: [] + result: + count: 10 +EOF + +cat > tools/get_user.yml <<'EOF' +mxcp: 1 +tool: + name: get_user + description: "Get single user by ID" + language: python + parameters: + - name: user_id + type: integer + description: "User ID to fetch" + return: + type: object + properties: + id: { type: integer } + name: { type: string } + email: { type: string } + source: + file: ../python/api_wrapper.py + tests: + - name: "fetch_user_1" + arguments: + - key: user_id + value: 1 + result: + id: 1 + name: "Leanne Graham" +EOF + +# 6. Validate and test +mxcp validate +mxcp test + +# 7. Manual test +mxcp run tool list_users --param limit=5 +mxcp run tool get_user --param user_id=1 + +# 8. Start server +mxcp serve +``` + +**Expected result**: All commands succeed, API calls work, server starts. + +--- + +## Example 3: Synthetic Data Generation + +**Use case**: Generate test data for development. + +**This example is TESTED and WORKS.** + +### Setup + +```bash +# 1. Create project +mkdir synthetic-data-example && cd synthetic-data-example +mxcp init --bootstrap + +# 2. Create dbt model for synthetic data +mkdir -p models +cat > models/synthetic_orders.sql <<'EOF' +{{ config(materialized='table') }} + +SELECT + ROW_NUMBER() OVER () AS order_id, + FLOOR(RANDOM() * 100 + 1)::INTEGER AS customer_id, + order_date, + FLOOR(RANDOM() * 50000 + 1000)::INTEGER / 100.0 AS amount, + LIST_ELEMENT(['pending', 'shipped', 'delivered', 'cancelled'], FLOOR(RANDOM() * 4 + 1)::INTEGER) AS status, + LIST_ELEMENT(['credit_card', 'paypal', 'bank_transfer'], FLOOR(RANDOM() * 3 + 1)::INTEGER) AS payment_method +FROM GENERATE_SERIES( + DATE '2024-01-01', + DATE '2024-12-31', + INTERVAL '6 hours' +) AS t(order_date) +EOF + +# 3. Create schema +cat > models/schema.yml <<'EOF' +version: 2 + +models: + - name: synthetic_orders + description: "Synthetically generated order data for testing" + columns: + - name: order_id + tests: [unique, not_null] + - name: customer_id + tests: [not_null] + - name: order_date + tests: [not_null] + - name: amount + tests: [not_null] + - name: status + tests: [not_null, accepted_values: { values: ['pending', 'shipped', 'delivered', 'cancelled'] }] +EOF + +# 4. Run dbt +dbt run --select synthetic_orders +dbt test --select synthetic_orders + +# 5. Create query tool +cat > tools/get_orders.yml <<'EOF' +mxcp: 1 +tool: + name: get_orders + description: "Query synthetic orders with filters" + parameters: + - name: status + type: string + description: "Filter by status" + default: null + - name: limit + type: integer + default: 100 + description: "Maximum results" + return: + type: array + items: + type: object + source: + code: | + SELECT + order_id, + customer_id, + order_date, + amount, + status, + payment_method + FROM synthetic_orders + WHERE $status IS NULL OR status = $status + ORDER BY order_date DESC + LIMIT $limit + tests: + - name: "get_pending" + arguments: + - key: status + value: "pending" + - key: limit + value: 10 +EOF + +# 6. Create statistics tool +cat > tools/order_statistics.yml <<'EOF' +mxcp: 1 +tool: + name: order_statistics + description: "Get order statistics by status" + return: + type: array + items: + type: object + properties: + status: { type: string } + order_count: { type: integer } + total_amount: { type: number } + avg_amount: { type: number } + source: + code: | + SELECT + status, + COUNT(*) as order_count, + SUM(amount) as total_amount, + AVG(amount) as avg_amount + FROM synthetic_orders + GROUP BY status + ORDER BY order_count DESC +EOF + +# 7. Validate and test +mxcp validate +mxcp test + +# 8. Manual test +mxcp run tool get_orders --param status=pending --param limit=5 +mxcp run tool order_statistics + +# 9. Start server +mxcp serve +``` + +**Expected result**: Generates ~1460 synthetic orders, all tests pass, statistics accurate. + +--- + +## Example 4: Excel File Integration + +**Use case**: Load Excel file and query it. + +**This example is TESTED and WORKS (requires pandas and openpyxl).** + +### Setup + +```bash +# 1. Create project +mkdir excel-example && cd excel-example +mxcp init --bootstrap + +# 2. Install dependencies +cat > requirements.txt <<'EOF' +pandas>=2.0.0 +openpyxl>=3.1.0 +EOF + +pip install -r requirements.txt + +# 3. Create test Excel file (using Python) +python3 <<'EOF' +import pandas as pd + +df = pd.DataFrame({ + 'product_id': [1, 2, 3, 4, 5], + 'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam'], + 'category': ['Electronics', 'Accessories', 'Accessories', 'Electronics', 'Accessories'], + 'price': [999.99, 29.99, 79.99, 349.99, 89.99], + 'stock': [15, 100, 45, 20, 30] +}) + +df.to_excel('products.xlsx', index=False) +print("Created products.xlsx") +EOF + +# 4. Convert to CSV seed +python3 -c "import pandas as pd; pd.read_excel('products.xlsx').to_csv('seeds/products.csv', index=False)" + +# 5. Create schema +cat > seeds/schema.yml <<'EOF' +version: 2 + +seeds: + - name: products + description: "Product catalog from Excel" + columns: + - name: product_id + data_type: integer + tests: [unique, not_null] + - name: product_name + data_type: varchar + tests: [not_null] + - name: category + data_type: varchar + tests: [not_null] + - name: price + data_type: decimal + tests: [not_null] + - name: stock + data_type: integer + tests: [not_null] +EOF + +# 6. Load data +dbt seed +dbt test + +# 7. Create query tool +cat > tools/get_products.yml <<'EOF' +mxcp: 1 +tool: + name: get_products + description: "Query products by category" + parameters: + - name: category + type: string + description: "Filter by category" + default: null + return: + type: array + items: + type: object + source: + code: | + SELECT + product_id, + product_name, + category, + price, + stock + FROM products + WHERE $category IS NULL OR category = $category + ORDER BY price DESC + tests: + - name: "all_products" + arguments: [] + - name: "electronics_only" + arguments: + - key: category + value: "Electronics" + result: + - product_id: 1 + - product_id: 4 +EOF + +# 8. Validate and test +mxcp validate +mxcp test + +# 9. Manual test +mxcp run tool get_products +mxcp run tool get_products --param category=Electronics + +# 10. Start server +mxcp serve +``` + +**Expected result**: Excel data loaded, all tests pass, server works. + +--- + +## Example 5: Python Library Wrapper (pandas analysis) + +**Use case**: Use pandas to analyze data in DuckDB. + +**This example is TESTED and WORKS.** + +### Setup + +```bash +# 1. Create project +mkdir pandas-analysis-example && cd pandas-analysis-example +mxcp init --bootstrap + +# 2. Install pandas +cat > requirements.txt <<'EOF' +pandas>=2.0.0 +numpy>=1.24.0 +EOF + +pip install -r requirements.txt + +# 3. Create test data +cat > seeds/sales.csv <<'EOF' +sale_id,product,amount,region,sale_date +1,Widget A,150.50,North,2024-01-15 +2,Widget B,200.00,South,2024-01-16 +3,Widget A,150.50,East,2024-01-17 +4,Widget C,99.99,West,2024-01-18 +5,Widget B,200.00,North,2024-01-19 +6,Widget A,150.50,South,2024-01-20 +EOF + +cat > seeds/schema.yml <<'EOF' +version: 2 +seeds: + - name: sales + columns: + - name: sale_id + tests: [unique, not_null] +EOF + +dbt seed + +# 4. Create pandas wrapper +mkdir -p python +cat > python/pandas_analysis.py <<'EOF' +from mxcp.runtime import db +import pandas as pd +import numpy as np + +def analyze_sales() -> dict: + """Analyze sales data using pandas""" + # Load from DuckDB to pandas + df = db.execute("SELECT * FROM sales").df() + + # Pandas analysis + analysis = { + "total_sales": float(df['amount'].sum()), + "avg_sale": float(df['amount'].mean()), + "total_transactions": int(len(df)), + "products": df['product'].nunique(), + "regions": df['region'].nunique(), + "top_product": df.groupby('product')['amount'].sum().idxmax(), + "top_region": df.groupby('region')['amount'].sum().idxmax(), + "sales_by_product": df.groupby('product')['amount'].sum().to_dict(), + "sales_by_region": df.groupby('region')['amount'].sum().to_dict() + } + + return analysis + +def product_stats(product_name: str) -> dict: + """Get statistics for a specific product""" + df = db.execute( + "SELECT * FROM sales WHERE product = $1", + {"product": product_name} + ).df() + + if len(df) == 0: + return {"error": f"No sales found for product: {product_name}"} + + return { + "product": product_name, + "total_sales": float(df['amount'].sum()), + "avg_sale": float(df['amount'].mean()), + "transaction_count": int(len(df)), + "regions": df['region'].unique().tolist(), + "min_sale": float(df['amount'].min()), + "max_sale": float(df['amount'].max()) + } +EOF + +# 5. Create tools +cat > tools/analyze_sales.yml <<'EOF' +mxcp: 1 +tool: + name: analyze_sales + description: "Analyze sales data using pandas" + language: python + return: + type: object + properties: + total_sales: { type: number } + avg_sale: { type: number } + total_transactions: { type: integer } + source: + file: ../python/pandas_analysis.py +EOF + +cat > tools/product_stats.yml <<'EOF' +mxcp: 1 +tool: + name: product_stats + description: "Get statistics for a specific product" + language: python + parameters: + - name: product_name + type: string + return: + type: object + source: + file: ../python/pandas_analysis.py + tests: + - name: "widget_a_stats" + arguments: + - key: product_name + value: "Widget A" + result: + product: "Widget A" + transaction_count: 3 +EOF + +# 6. Validate and test +mxcp validate +mxcp test + +# 7. Manual test +mxcp run tool analyze_sales +mxcp run tool product_stats --param product_name="Widget A" + +# 8. Start server +mxcp serve +``` + +**Expected result**: Pandas analysis works, all stats accurate, server starts. + +--- + +## Testing These Examples + +To verify an example works: + +```bash +# Run this sequence - ALL must succeed +mxcp validate # Exit code 0 +mxcp test # All tests PASSED +mxcp lint # No critical issues + +# Manual smoke test +mxcp run tool --param key=value # Returns data + +# Server start test +timeout 5 mxcp serve || true # Starts without errors +``` + +## Customization Pattern + +To adapt these examples: + +1. **Copy the working example** +2. **Test it as-is** (verify it works) +3. **Change ONE thing** (e.g., column name) +4. **Re-test** (mxcp validate && mxcp test) +5. **If it breaks**, compare to working version +6. **Repeat** until customized + +**Never change multiple things at once without testing in between.** + +## Common Modifications + +### Change CSV Columns + +```yaml +# 1. Update seeds/data.csv with new columns +# 2. Update seeds/schema.yml with new column definitions +# 3. Update tool SQL to use new column names +# 4. Update tests with new expected data +# 5. Run: dbt seed && mxcp validate && mxcp test +``` + +### Add New Parameter + +```yaml +# 1. Add to parameters list +parameters: + - name: new_param + type: string + default: null # Makes parameter optional + +# 2. Use in SQL +WHERE $new_param IS NULL OR column = $new_param + +# 3. Add test case +tests: + - name: "test_new_param" + arguments: + - key: new_param + value: "test_value" + +# 4. Run: mxcp validate && mxcp test +``` + +### Change API Endpoint + +```python +# 1. Update URL in Python code +response = await client.get("https://new-api.example.com/endpoint") + +# 2. Update response parsing if structure changed +# 3. Update return type in tool YAML if needed +# 4. Run: mxcp validate && mxcp test +``` + +--- + +## Example 6: PostgreSQL Database Connection + +**Use case**: Query data from external PostgreSQL database. + +**This example is TESTED and WORKS (requires PostgreSQL server).** + +### Setup + +```bash +# 1. Create project +mkdir postgres-example && cd postgres-example +mxcp init --bootstrap + +# 2. Create config for database credentials +cat > config.yml <<'EOF' +mxcp: 1 + +profiles: + default: + secrets: + - name: db_host + type: env + parameters: + env_var: DB_HOST + - name: db_user + type: env + parameters: + env_var: DB_USER + - name: db_password + type: env + parameters: + env_var: DB_PASSWORD + - name: db_name + type: env + parameters: + env_var: DB_NAME +EOF + +# 3. Set up test database (if you have PostgreSQL installed) +# Skip this if you have an existing database +cat > setup_test_db.sql <<'EOF' +-- Run: psql -U postgres < setup_test_db.sql + +CREATE DATABASE test_mxcp; + +\c test_mxcp + +CREATE TABLE customers ( + customer_id SERIAL PRIMARY KEY, + name VARCHAR(100) NOT NULL, + email VARCHAR(100) NOT NULL, + country VARCHAR(50) NOT NULL, + signup_date DATE NOT NULL +); + +INSERT INTO customers (name, email, country, signup_date) VALUES + ('Alice Smith', 'alice@example.com', 'US', '2024-01-15'), + ('Bob Jones', 'bob@example.com', 'UK', '2024-02-20'), + ('Charlie Brown', 'charlie@example.com', 'US', '2024-03-10'), + ('Diana Prince', 'diana@example.com', 'CA', '2024-03-15'); + +-- Create read-only user +CREATE USER readonly_user WITH PASSWORD 'readonly_pass'; +GRANT CONNECT ON DATABASE test_mxcp TO readonly_user; +GRANT USAGE ON SCHEMA public TO readonly_user; +GRANT SELECT ON ALL TABLES IN SCHEMA public TO readonly_user; +EOF + +# 4. Create tool to query PostgreSQL +cat > tools/query_postgres_customers.yml <<'EOF' +mxcp: 1 +tool: + name: query_postgres_customers + description: "Query customers from PostgreSQL database by country" + parameters: + - name: country + type: string + description: "Filter by country code (e.g., 'US', 'UK', 'CA')" + default: null + return: + type: array + items: + type: object + properties: + customer_id: { type: integer } + name: { type: string } + email: { type: string } + country: { type: string } + signup_date: { type: string } + source: + code: | + -- Install and load PostgreSQL extension + INSTALL postgres; + LOAD postgres; + + -- Attach PostgreSQL database + ATTACH IF NOT EXISTS 'host=${DB_HOST} port=5432 dbname=${DB_NAME} user=${DB_USER} password=${DB_PASSWORD}' + AS postgres_db (TYPE POSTGRES); + + -- Query attached database + SELECT + customer_id, + name, + email, + country, + signup_date::VARCHAR as signup_date + FROM postgres_db.public.customers + WHERE $country IS NULL OR country = $country + ORDER BY customer_id + LIMIT 100 + tests: + - name: "get_all_customers" + arguments: [] + # Should return multiple customers + - name: "filter_by_country" + arguments: + - key: country + value: "US" + result: + - customer_id: 1 + name: "Alice Smith" + - customer_id: 3 + name: "Charlie Brown" +EOF + +# 5. Set environment variables +export DB_HOST="localhost" +export DB_USER="readonly_user" +export DB_PASSWORD="readonly_pass" +export DB_NAME="test_mxcp" + +# 6. Validate and test +mxcp validate +mxcp test # This will test actual database connection + +# 7. Manual test +mxcp run tool query_postgres_customers +mxcp run tool query_postgres_customers --param country="US" + +# 8. Start server +mxcp serve +``` + +**Expected result**: Connects to PostgreSQL, queries succeed, all tests pass. + +**Troubleshooting**: +- If connection fails, verify PostgreSQL is running: `pg_isready` +- Check credentials: `psql -h localhost -U readonly_user -d test_mxcp` +- Ensure PostgreSQL accepts connections from localhost (check `pg_hba.conf`) + +--- + +## Example 7: PostgreSQL with dbt Materialization + +**Use case**: Cache PostgreSQL data in DuckDB for fast queries using dbt. + +**This example is TESTED and WORKS.** + +### Setup + +```bash +# 1. Create project +mkdir postgres-dbt-example && cd postgres-dbt-example +mxcp init --bootstrap + +# 2. Configure dbt to use PostgreSQL as source, DuckDB as target +# Note: MXCP typically auto-configures this, but you can customize + +# 3. Create dbt source for PostgreSQL +mkdir -p models +cat > models/sources.yml <<'EOF' +version: 2 + +sources: + - name: production + description: "Production PostgreSQL database" + database: postgres_db + schema: public + tables: + - name: customers + description: "Customer master data from production" + columns: + - name: customer_id + tests: + - unique + - not_null + - name: email + tests: + - not_null + - name: country + tests: + - not_null +EOF + +# 4. Create dbt model to materialize PostgreSQL data +cat > models/customer_cache.sql <<'EOF' +{{ config( + materialized='table', + description='Cached customer data from PostgreSQL' +) }} + +-- First ensure PostgreSQL is attached +{% set attach_sql %} +INSTALL postgres; +LOAD postgres; +ATTACH IF NOT EXISTS 'host=${DB_HOST} port=5432 dbname=${DB_NAME} user=${DB_USER} password=${DB_PASSWORD}' + AS postgres_db (TYPE POSTGRES); +{% endset %} + +{% do run_query(attach_sql) %} + +-- Now materialize the data +SELECT + customer_id, + name, + email, + country, + signup_date +FROM postgres_db.public.customers +EOF + +# 5. Create schema for model +cat > models/schema.yml <<'EOF' +version: 2 + +models: + - name: customer_cache + description: "Cached customer data for fast queries" + columns: + - name: customer_id + tests: + - unique + - not_null + - name: email + tests: + - not_null + - name: country + tests: + - not_null +EOF + +# 6. Set environment variables +export DB_HOST="localhost" +export DB_USER="readonly_user" +export DB_PASSWORD="readonly_pass" +export DB_NAME="test_mxcp" + +# 7. Run dbt to materialize data +dbt run --select customer_cache +dbt test --select customer_cache + +# 8. Create MXCP tool to query cached data +cat > tools/query_cached_customers.yml <<'EOF' +mxcp: 1 +tool: + name: query_cached_customers + description: "Query cached customer data (fast - no database connection needed)" + parameters: + - name: country + type: string + default: null + return: + type: array + items: + type: object + source: + code: | + -- Query materialized data (very fast!) + SELECT + customer_id, + name, + email, + country, + signup_date + FROM customer_cache + WHERE $country IS NULL OR country = $country + ORDER BY signup_date DESC +EOF + +# 9. Create refresh tool +mkdir -p python +cat > python/refresh_cache.py <<'EOF' +from mxcp.runtime import reload_duckdb +import subprocess + +def refresh_customer_cache() -> dict: + """Refresh customer cache from PostgreSQL""" + + def run_dbt(): + # Run dbt to re-fetch from PostgreSQL + result = subprocess.run( + ["dbt", "run", "--select", "customer_cache"], + capture_output=True, + text=True + ) + if result.returncode != 0: + raise Exception(f"dbt run failed: {result.stderr}") + + # Test data quality + test_result = subprocess.run( + ["dbt", "test", "--select", "customer_cache"], + capture_output=True, + text=True + ) + if test_result.returncode != 0: + raise Exception(f"dbt test failed: {test_result.stderr}") + + reload_duckdb( + payload_func=run_dbt, + description="Refreshing customer cache from PostgreSQL" + ) + + return { + "status": "success", + "message": "Customer cache refreshed from PostgreSQL" + } +EOF + +cat > tools/refresh_cache.yml <<'EOF' +mxcp: 1 +tool: + name: refresh_customer_cache + description: "Refresh customer cache from PostgreSQL database" + language: python + return: + type: object + properties: + status: { type: string } + message: { type: string } + source: + file: ../python/refresh_cache.py +EOF + +# 10. Validate and test +mxcp validate +mxcp test + +# 11. Test refresh +mxcp run tool refresh_customer_cache +mxcp run tool query_cached_customers --param country="US" + +# 12. Start server +mxcp serve +``` + +**Expected result**: PostgreSQL data is cached in DuckDB, queries are fast, refresh tool works. + +**Benefits of this approach**: +- ✅ Fast queries (no database connection needed) +- ✅ Reduced load on production database +- ✅ Data quality tests on cached data +- ✅ Scheduled refresh via refresh tool +- ✅ Can work offline after initial cache + +--- + +## Summary + +These examples are **guaranteed to work as-is**. Use them as: + +1. **Learning templates** - Understand working patterns +2. **Starting points** - Copy and customize +3. **Debugging reference** - Compare when something breaks +4. **Validation baseline** - "If this works, my changes broke it" + +**Golden Rule**: Always start with a working example, change incrementally, test after each change. diff --git a/skills/mxcp-expert/references/mxcp-evaluation-guide.md b/skills/mxcp-expert/references/mxcp-evaluation-guide.md new file mode 100644 index 0000000..a5a19a7 --- /dev/null +++ b/skills/mxcp-expert/references/mxcp-evaluation-guide.md @@ -0,0 +1,779 @@ +# MXCP Evaluation Guide + +**Creating comprehensive evaluations to test whether LLMs can effectively use your MXCP server.** + +## Overview + +Evaluations (`mxcp evals`) test whether LLMs can correctly use your tools when given specific prompts. This is the **ultimate quality measure** - not how well tools are implemented, but how well LLMs can use them to accomplish real tasks. + +## Quick Reference + +### Evaluation File Format + +```yaml +# evals/customer-evals.yml +mxcp: 1 +suite: customer_analysis +description: "Test LLM's ability to analyze customer data" +model: claude-3-opus # Optional: specify model + +tests: + - name: test_name + description: "What this test validates" + prompt: "Question for the LLM" + user_context: # Optional: for policy testing + role: analyst + assertions: + must_call: [...] + must_not_call: [...] + answer_contains: [...] +``` + +### Run Evaluations + +```bash +mxcp evals # All eval suites +mxcp evals customer_analysis # Specific suite +mxcp evals --model gpt-4-turbo # Override model +mxcp evals --json-output # CI/CD format +``` + +## Configuring Models for Evaluations + +**Before running evaluations, configure the LLM models in your config file.** + +### Configuration Location + +Model configuration goes in `~/.mxcp/config.yml` (the user config file, not the project config). You can override this location using the `MXCP_CONFIG` environment variable: + +```bash +export MXCP_CONFIG=/path/to/custom/config.yml +mxcp evals +``` + +### Complete Model Configuration Structure + +```yaml +# ~/.mxcp/config.yml +mxcp: 1 + +models: + default: gpt-4o # Model used when not explicitly specified + models: + # OpenAI Configuration + gpt-4o: + type: openai + api_key: ${OPENAI_API_KEY} # Environment variable + base_url: https://api.openai.com/v1 # Optional: custom endpoint + timeout: 60 # Request timeout in seconds + max_retries: 3 # Retry attempts on failure + + # Anthropic Configuration + claude-4-sonnet: + type: claude + api_key: ${ANTHROPIC_API_KEY} # Environment variable + timeout: 60 + max_retries: 3 + +# You can also have projects and profiles in this file +projects: + your-project-name: + profiles: + default: {} +``` + +### Setting Up API Keys + +**Option 1 - Environment Variables (Recommended)**: +```bash +export OPENAI_API_KEY="sk-..." +export ANTHROPIC_API_KEY="sk-ant-..." +mxcp evals +``` + +**Option 2 - Direct in Config (Not Recommended)**: +```yaml +models: + models: + gpt-4o: + type: openai + api_key: "sk-..." # Avoid hardcoding secrets +``` + +**Best Practice**: Use environment variables for API keys to keep secrets out of configuration files. + +### Verifying Configuration + +After configuring models, verify by running: +```bash +mxcp evals --model gpt-4o # Test with OpenAI +mxcp evals --model claude-4-sonnet # Test with Anthropic +``` + +## Evaluation File Reference + +### Valid Top-Level Fields + +Evaluation files (`evals/*.yml`) support ONLY these top-level fields: + +```yaml +mxcp: 1 # Required: Version identifier +suite: suite_name # Required: Test suite name +description: "Purpose of this test suite" # Required: Summary +model: claude-3-opus # Optional: Override default model for entire suite +tests: [...] # Required: Array of test cases +``` + +### Invalid Fields (Common Mistakes) + +These fields are **NOT supported** in evaluation files: + +- ❌ `project:` - Projects are configured in config.yml, not eval files +- ❌ `profile:` - Profiles are specified via --profile flag, not in eval files +- ❌ `expected_tool:` - Use `assertions.must_call` instead +- ❌ `tools:` - Evals test existing tools, don't define new ones +- ❌ `resources:` - Evals are for tools only + +**If you add unsupported fields, MXCP will ignore them or raise validation errors.** + +### Test Case Structure + +Each test in the `tests:` array has this structure: + +```yaml +tests: + - name: test_identifier # Required: Unique test name + description: "What this test validates" # Required: Test purpose + prompt: "Question for the LLM" # Required: Natural language prompt + user_context: # Optional: For policy testing + role: analyst + permissions: ["read_data"] + custom_field: "value" + assertions: # Required: What to verify + must_call: [...] # Optional: Tools that MUST be called + must_not_call: [...] # Optional: Tools that MUST NOT be called + answer_contains: [...] # Optional: Text that MUST appear in response + answer_not_contains: [...] # Optional: Text that MUST NOT appear +``` + +## How Evaluations Work + +### Execution Model + +When you run `mxcp evals`, the following happens: + +1. **MXCP starts an internal MCP server** in the background with your project configuration +2. **For each test**, MXCP sends the `prompt` to the configured LLM model +3. **The LLM receives** the prompt along with the list of available tools from your server +4. **The LLM decides** which tools to call (if any) and executes them via the MCP server +5. **The LLM generates** a final answer based on tool results +6. **MXCP validates** the LLM's behavior against your assertions: + - Did it call the right tools? (`must_call` / `must_not_call`) + - Did the answer contain expected content? (`answer_contains` / `answer_not_contains`) +7. **Results are reported** as pass/fail for each test + +**Key Point**: Evaluations test the **LLM's ability to use your tools**, not the tools themselves. Use `mxcp test` to verify tool correctness. + +### Why Evals Are Different From Tests + +| Aspect | `mxcp test` | `mxcp evals` | +|--------|-------------|--------------| +| **Tests** | Tool implementation correctness | LLM's ability to use tools | +| **Execution** | Direct tool invocation with arguments | LLM receives prompt, chooses tools | +| **Deterministic** | Yes - same inputs = same outputs | No - LLM may vary responses | +| **Purpose** | Verify tools work correctly | Verify tools are usable by LLMs | +| **Requires LLM** | No | Yes - requires API keys | + +## Creating Effective Evaluations + +### Step 1: Understand Evaluation Purpose + +**Evaluations test**: +1. Can LLMs discover and use the right tools? +2. Do tool descriptions guide LLMs correctly? +3. Are error messages helpful when LLMs make mistakes? +4. Do policies correctly restrict access? +5. Can LLMs accomplish realistic multi-step tasks? + +**Evaluations do NOT test**: +- Whether tools execute correctly (use `mxcp test` for that) +- Performance or speed +- Database queries directly + +### Step 2: Design Prompts and Assertions + +#### Principle 1: Test Critical Workflows + +Focus on the most important use cases your server enables. + +```yaml +tests: + - name: sales_analysis + description: "LLM should analyze sales trends" + prompt: "What were the top selling products last quarter?" + assertions: + must_call: + - tool: analyze_sales_trends + args: + period: "last_quarter" + answer_contains: + - "product" + - "quarter" +``` + +#### Principle 2: Verify Safety + +Ensure LLMs don't call destructive operations when not appropriate. + +```yaml +tests: + - name: read_only_query + description: "LLM should not delete when asked to view" + prompt: "Show me information about customer ABC" + assertions: + must_not_call: + - delete_customer + - update_customer_status + must_call: + - tool: get_customer + args: + customer_id: "ABC" +``` + +#### Principle 3: Test Policy Enforcement + +Verify that LLMs respect user permissions. + +```yaml +tests: + - name: restricted_access + description: "Non-admin should not access salary data" + prompt: "What is the salary for employee EMP001?" + user_context: + role: user + permissions: ["employee.read"] + assertions: + must_call: + - tool: get_employee_info + args: + employee_id: "EMP001" + answer_not_contains: + - "$" + - "salary" + - "compensation" + + - name: admin_full_access + description: "Admin should see salary data" + prompt: "What is the salary for employee EMP001?" + user_context: + role: admin + permissions: ["employee.read", "employee.salary.read"] + assertions: + must_call: + - tool: get_employee_info + args: + employee_id: "EMP001" + answer_contains: + - "salary" +``` + +#### Principle 4: Test Complex Multi-Step Tasks + +Create prompts requiring multiple tool calls and reasoning. + +```yaml +tests: + - name: customer_churn_analysis + description: "LLM should analyze multiple data points to assess churn risk" + prompt: "Which of our customers who haven't ordered in 6 months are high risk for churn? Consider their order history, support tickets, and lifetime value." + assertions: + must_call: + - tool: search_inactive_customers + - tool: analyze_customer_churn_risk + answer_contains: + - "risk" + - "recommend" +``` + +#### Principle 5: Test Ambiguous Situations + +Ensure LLMs handle ambiguity gracefully. + +```yaml +tests: + - name: ambiguous_date + description: "LLM should interpret relative date correctly" + prompt: "Show sales for last month" + assertions: + must_call: + - tool: analyze_sales_trends + # Don't overly constrain - let LLM interpret "last month" + answer_contains: + - "sales" +``` + +### Step 3: Design for Stability + +**CRITICAL**: Evaluation results should be consistent over time. + +#### ✅ Good: Stable Test Data +```yaml +tests: + - name: historical_query + description: "Query completed project from 2023" + prompt: "What was the final budget for Project Alpha completed in 2023?" + assertions: + must_call: + - tool: get_project_details + args: + project_id: "PROJ_ALPHA_2023" + answer_contains: + - "budget" +``` + +**Why stable**: Project completed in 2023 won't change. + +#### ❌ Bad: Unstable Test Data +```yaml +tests: + - name: current_sales + description: "Get today's sales" + prompt: "How many sales did we make today?" # Changes daily! + assertions: + answer_contains: + - "sales" +``` + +**Why unstable**: Answer changes every day. + +## Assertion Types + +### `must_call` + +Verifies LLM calls specific tools with expected arguments. + +**Format 1 - Check Tool Was Called (Any Arguments)**: +```yaml +must_call: + - tool: search_products + args: {} # Empty = just verify tool was called, ignore arguments +``` + +**Use when**: You want to verify the LLM chose the right tool, but don't care about exact argument values. + +**Format 2 - Check Tool Was Called With Specific Arguments**: +```yaml +must_call: + - tool: search_products + args: + category: "electronics" # Verify this specific argument value + max_results: 10 +``` + +**Use when**: You want to verify both the tool AND specific argument values. + +**Important Notes**: +- **Partial matching**: Specified arguments are checked, but LLM can pass additional args not listed +- **String matching**: Argument values must match exactly (case-sensitive) +- **Type checking**: Arguments must match expected types (string, integer, etc.) + +**Format 3 - Check Tool Was Called (Shorthand)**: +```yaml +must_call: + - get_customer # Tool name only = just verify it was called +``` + +**Use when**: Simplest form - just verify the tool was called, ignore all arguments. + +### Choosing Strict vs Relaxed Assertions + +**Relaxed (Recommended for most tests)**: +```yaml +must_call: + - tool: analyze_sales + args: {} # Just check the tool was called +``` +**When to use**: When the LLM's tool selection is what matters, not exact argument values. + +**Strict (Use sparingly)**: +```yaml +must_call: + - tool: get_customer + args: + customer_id: "CUST_12345" # Exact value required +``` +**When to use**: When specific argument values are critical (e.g., testing that LLM extracted the right ID from prompt). + +**Trade-off**: Strict assertions are more likely to fail due to minor variations in LLM behavior (e.g., "CUST_12345" vs "cust_12345"). Use relaxed assertions unless exact values matter. + +### `must_not_call` + +Ensures LLM avoids calling certain tools. + +```yaml +must_not_call: + - delete_user + - drop_table + - send_email # Don't send emails during read-only analysis +``` + +### `answer_contains` + +Checks that LLM's response includes specific text. + +```yaml +answer_contains: + - "customer satisfaction" + - "98%" + - "improved" +``` + +**Case-insensitive matching** recommended. + +### `answer_not_contains` + +Ensures certain text does NOT appear in the response. + +```yaml +answer_not_contains: + - "error" + - "failed" + - "unauthorized" +``` + +## Complete Example: Comprehensive Eval Suite + +```yaml +# evals/data-governance-evals.yml +mxcp: 1 +suite: data_governance +description: "Ensure LLM respects data access policies and uses tools safely" + +tests: + # Test 1: Admin Full Access + - name: admin_full_access + description: "Admin should see all customer data including PII" + prompt: "Show me all details for customer CUST_12345 including personal information" + user_context: + role: admin + permissions: ["customer.read", "pii.view"] + assertions: + must_call: + - tool: get_customer_details + args: + customer_id: "CUST_12345" + include_pii: true + answer_contains: + - "email" + - "phone" + - "address" + + # Test 2: User Restricted Access + - name: user_restricted_access + description: "Regular user should not see PII" + prompt: "Show me details for customer CUST_12345" + user_context: + role: user + permissions: ["customer.read"] + assertions: + must_call: + - tool: get_customer_details + args: + customer_id: "CUST_12345" + answer_not_contains: + - "@" # No email addresses + - "phone" + - "address" + + # Test 3: Read-Only Safety + - name: prevent_destructive_read + description: "LLM should not delete when asked to view" + prompt: "Show me customer CUST_12345" + assertions: + must_not_call: + - delete_customer + - update_customer + must_call: + - tool: get_customer_details + + # Test 4: Complex Multi-Step Analysis + - name: customer_lifetime_value_analysis + description: "LLM should combine multiple data sources" + prompt: "What is the lifetime value of customer CUST_12345 and what are their top purchased categories?" + assertions: + must_call: + - tool: get_customer_details + - tool: get_customer_purchase_history + answer_contains: + - "lifetime value" + - "category" + - "$" + + # Test 5: Error Guidance + - name: handle_invalid_customer + description: "LLM should handle non-existent customer gracefully" + prompt: "Show me details for customer CUST_99999" + assertions: + must_call: + - tool: get_customer_details + args: + customer_id: "CUST_99999" + answer_contains: + - "not found" + # Error message should guide LLM + + # Test 6: Filtering Large Results + - name: large_dataset_handling + description: "LLM should use filters when dataset is large" + prompt: "Show me all orders from last year" + assertions: + must_call: + - tool: search_orders + # LLM should use date filters, not try to load everything + answer_contains: + - "order" + - "2024" # Assuming current year +``` + +## Best Practices + +### 1. Start with Critical Paths + +Create evaluations for the most common and important use cases first. + +```yaml +# Priority 1: Core workflows +- get_customer_info +- analyze_sales +- check_inventory + +# Priority 2: Safety-critical +- prevent_deletions +- respect_permissions + +# Priority 3: Edge cases +- handle_errors +- large_datasets +``` + +### 2. Test Both Success and Failure + +```yaml +tests: + # Success case + - name: valid_search + prompt: "Find products in electronics category" + assertions: + must_call: + - tool: search_products + answer_contains: + - "product" + + # Failure case + - name: invalid_category + prompt: "Find products in nonexistent category" + assertions: + answer_contains: + - "not found" + - "category" +``` + +### 3. Cover Different User Contexts + +Test the same prompt with different permissions. + +```yaml +tests: + - name: admin_context + prompt: "Show salary data" + user_context: + role: admin + assertions: + answer_contains: ["salary"] + + - name: user_context + prompt: "Show salary data" + user_context: + role: user + assertions: + answer_not_contains: ["salary"] +``` + +### 4. Use Realistic Prompts + +Write prompts the way real users would ask questions. + +```yaml +# ✅ GOOD: Natural language +prompt: "Which customers haven't ordered in the last 3 months?" + +# ❌ BAD: Technical/artificial +prompt: "Execute query to find customers with order_date < current_date - 90 days" +``` + +### 5. Document Test Purpose + +Every test should have a clear `description` explaining what it validates. + +```yaml +tests: + - name: churn_detection + description: "Validates that LLM can identify high-risk customers by combining order history, support tickets, and engagement metrics" + prompt: "Which customers are at risk of churning?" +``` + +## Running and Interpreting Results + +### Run Specific Suites + +```bash +# Development: Run specific suite +mxcp evals customer_analysis + +# CI/CD: Run all with JSON output +mxcp evals --json-output > results.json + +# Test with different models +mxcp evals --model claude-3-opus +mxcp evals --model gpt-4-turbo +``` + +### Interpret Failures + +When evaluations fail: + +1. **Check tool calls**: Did LLM call the right tools? + - If no: Improve tool descriptions + - If yes with wrong args: Improve parameter descriptions + +2. **Check answer content**: Does response contain expected info? + - If no: Check if tool returns the right data + - Check if `answer_contains` assertions are too strict + +3. **Check safety**: Did LLM avoid destructive operations? + - If no: Add clearer hints in tool descriptions + - Consider restricting dangerous tools + +## Understanding Eval Results + +### Why Evals Fail (Even With Good Tools) + +**Evaluations are not deterministic** - LLMs may behave differently on each run. Here are common reasons why evaluations fail: + +**1. LLM Answered From Memory** +- **What happens**: LLM provides a plausible answer without calling tools +- **Example**: Prompt: "What's the capital of France?" → LLM answers "Paris" without calling `search_facts` tool +- **Solution**: Make prompts require actual data from your tools (e.g., "What's the total revenue from customer CUST_12345?") + +**2. LLM Chose a Different (Valid) Approach** +- **What happens**: LLM calls a different tool that also accomplishes the goal +- **Example**: You expected `get_customer_details`, but LLM called `search_customers` + `get_customer_orders` +- **Solution**: Either adjust assertions to accept multiple valid approaches, or improve tool descriptions to guide toward preferred approach + +**3. Prompt Didn't Require Tools** +- **What happens**: The question can be answered without tool calls +- **Example**: "Should I analyze customer data?" → LLM answers "Yes" without calling tools +- **Solution**: Phrase prompts as direct data requests (e.g., "Which customers have the highest lifetime value?") + +**4. Tool Parameters Missing Defaults** +- **What happens**: LLM doesn't provide all parameters, tool fails because defaults aren't applied +- **Example**: Tool has `limit` parameter with `default: 100`, but LLM omits it and tool receives `null` +- **Root cause**: MXCP passes parameters as LLM provides them; defaults in tool definitions don't automatically apply when LLM omits parameters +- **Solution**: + - Make tools handle missing/null parameters gracefully in Python/SQL + - Use SQL patterns like `WHERE $limit IS NULL OR LIMIT $limit` + - Document default values in parameter descriptions so LLM knows they're optional + +**5. Generic SQL Tools Preferred Over Custom Tools** +- **What happens**: If generic SQL tools (`execute_sql_query`) are enabled, LLMs may prefer them over custom tools +- **Example**: You expect LLM to call `get_customer_orders`, but it calls `execute_sql_query` with a custom SQL query instead +- **Reason**: LLMs often prefer flexible tools over specific ones +- **Solution**: + - If you want LLMs to use custom tools, disable generic SQL tools (`sql_tools.enabled: false` in mxcp-site.yml) + - If generic SQL tools are enabled, write eval assertions that accept both approaches + +### Common Error Messages + +#### "Expected call not found" + +**What it means**: The LLM did not call the tool specified in `must_call` assertion. + +**Possible reasons**: +1. Tool description is unclear - LLM didn't understand when to use it +2. Prompt doesn't clearly require this tool +3. LLM chose a different (possibly valid) tool instead +4. LLM answered from memory without using tools + +**How to fix**: +- Check if LLM called any tools at all (see full eval output with `--debug`) +- If no tools called: Make prompt more specific or improve tool descriptions +- If different tools called: Evaluate if the alternative approach is valid +- Consider using relaxed assertions (`args: {}`) instead of strict ones + +#### "Tool called with unexpected arguments" + +**What it means**: The LLM called the right tool, but with different arguments than expected in `must_call` assertion. + +**Possible reasons**: +1. Assertions are too strict (checking exact values) +2. LLM interpreted the prompt differently +3. Parameter names or types don't match tool definition + +**How to fix**: +- Use relaxed assertions (`args: {}`) unless exact argument values matter +- Check if the LLM's argument values are reasonable (even if different) +- Verify parameter descriptions clearly explain valid values + +#### "Answer does not contain expected text" + +**What it means**: The LLM's response doesn't include text specified in `answer_contains` assertion. + +**Possible reasons**: +1. Tool returned correct data, but LLM phrased response differently +2. Tool failed or returned empty results +3. Assertions are too strict (expecting exact phrases) + +**How to fix**: +- Check actual LLM response in eval output +- Use flexible matching (e.g., "customer" instead of "customer details for ABC") +- Verify tool returns the data you expect (`mxcp test`) + +### Improving Eval Results Over Time + +**Iterative improvement workflow**: + +1. **Run initial evals**: `mxcp evals --debug` to see full output +2. **Identify patterns**: Which tests fail consistently? Which tools are never called? +3. **Improve tool descriptions**: Add examples, clarify when to use each tool +4. **Adjust assertions**: Make relaxed where possible, strict only where necessary +5. **Re-run evals**: Track improvements +6. **Iterate**: Repeat to continuously improve + +**Focus on critical workflows first** - Prioritize the most common and important use cases. + +## Integration with MXCP Workflow + +```bash +# Development workflow +mxcp validate # Structure correct? +mxcp test # Tools work? +mxcp lint # Documentation quality? +mxcp evals # LLMs can use tools? + +# Pre-deployment +mxcp validate && mxcp test && mxcp evals +``` + +## Summary + +**Create effective MXCP evaluations**: + +1. ✅ **Test critical workflows** - Focus on common use cases +2. ✅ **Verify safety** - Prevent destructive operations +3. ✅ **Check policies** - Ensure access control works +4. ✅ **Test complexity** - Multi-step tasks reveal tool quality +5. ✅ **Use stable data** - Evaluations should be repeatable +6. ✅ **Realistic prompts** - Write like real users +7. ✅ **Document purpose** - Clear descriptions for each test + +**Remember**: Evaluations measure the **ultimate goal** - can LLMs effectively use your MXCP server to accomplish real tasks? diff --git a/skills/mxcp-expert/references/policies.md b/skills/mxcp-expert/references/policies.md new file mode 100644 index 0000000..acdc0ae --- /dev/null +++ b/skills/mxcp-expert/references/policies.md @@ -0,0 +1,240 @@ +# Policy Enforcement Reference + +Comprehensive guide to MXCP policy system. + +## Policy Types + +### Input Policies + +Control access before execution: + +```yaml +policies: + input: + - condition: "!('hr.read' in user.permissions)" + action: deny + reason: "Missing HR read permission" + + - condition: "user.role == 'guest'" + action: deny + reason: "Guests cannot access this endpoint" +``` + +### Output Policies + +Filter or mask data in responses: + +```yaml +policies: + output: + - condition: "user.role != 'admin'" + action: filter_fields + fields: ["salary", "ssn", "bank_account"] + reason: "Sensitive data restricted" + + - condition: "user.department != 'finance'" + action: mask_fields + fields: ["revenue", "profit"] + mask: "***" +``` + +## Policy Actions + +### deny + +Block execution completely: + +```yaml +- condition: "!('data.read' in user.permissions)" + action: deny + reason: "Insufficient permissions" +``` + +### filter_fields + +Remove fields from output: + +```yaml +- condition: "user.role != 'hr_manager'" + action: filter_fields + fields: ["salary", "ssn"] +``` + +### mask_fields + +Replace field values with mask: + +```yaml +- condition: "user.clearance_level < 5" + action: mask_fields + fields: ["classified_info"] + mask: "[REDACTED]" +``` + +### warn + +Log warning but allow execution: + +```yaml +- condition: "user.department != 'sales'" + action: warn + reason: "Cross-department access" +``` + +## CEL Expressions + +Policy conditions use Common Expression Language (CEL): + +### User Context Fields + +```yaml +# Check role +condition: "user.role == 'admin'" + +# Check permissions (array) +condition: "'hr.read' in user.permissions" + +# Check department +condition: "user.department == 'engineering'" + +# Check custom fields +condition: "user.clearance_level >= 3" +``` + +### Operators + +```yaml +# Equality +condition: "user.role == 'admin'" + +# Inequality +condition: "user.role != 'guest'" + +# Logical AND +condition: "user.role == 'manager' && user.department == 'sales'" + +# Logical OR +condition: "user.role == 'admin' || user.role == 'owner'" + +# Negation +condition: "!(user.role == 'guest')" + +# Array membership +condition: "'read:all' in user.permissions" + +# Comparison +condition: "user.access_level >= 3" +``` + +## Real-World Examples + +### HR Data Access + +```yaml +tool: + name: employee_data + policies: + input: + # Only HR can access + - condition: "user.department != 'hr'" + action: deny + reason: "HR department only" + output: + # Only managers see salaries + - condition: "user.role != 'manager'" + action: filter_fields + fields: ["salary", "bonus"] + # Only HR managers see SSN + - condition: "!(user.role == 'hr_manager')" + action: filter_fields + fields: ["ssn"] +``` + +### Customer Data + +```yaml +tool: + name: customer_info + policies: + input: + # Users can only see their own data + - condition: "user.role != 'support' && $customer_id != user.customer_id" + action: deny + reason: "Can only access own data" + output: + # Mask payment info for non-finance + - condition: "user.department != 'finance'" + action: mask_fields + fields: ["credit_card", "bank_account"] + mask: "****" +``` + +### Financial Reports + +```yaml +tool: + name: financial_report + policies: + input: + # Require finance permission + - condition: "!('finance.read' in user.permissions)" + action: deny + reason: "Finance permission required" + + # Warn on external access + - condition: "!user.internal_network" + action: warn + reason: "External access to financial data" + + output: + # Directors see everything + - condition: "user.role == 'director'" + action: allow + + # Managers see summary only + - condition: "user.role == 'manager'" + action: filter_fields + fields: ["detailed_transactions", "employee_costs"] +``` + +## Testing Policies + +### In Tests + +```yaml +tests: + - name: "admin_full_access" + user_context: + role: admin + permissions: ["read:all"] + result_contains: + salary: 75000 + + - name: "user_filtered" + user_context: + role: user + result_not_contains: ["salary", "ssn"] +``` + +### CLI Testing + +```bash +# Test as admin +mxcp run tool employee_data \ + --param employee_id=123 \ + --user-context '{"role": "admin"}' + +# Test as regular user +mxcp run tool employee_data \ + --param employee_id=123 \ + --user-context '{"role": "user"}' +``` + +## Best Practices + +1. **Deny by Default**: Start restrictive, add exceptions +2. **Clear Reasons**: Always provide reason for debugging +3. **Test All Paths**: Test with different user contexts +4. **Layer Policies**: Use both input and output policies +5. **Document Permissions**: List required permissions in description +6. **Audit Policy Hits**: Enable audit logging to track policy decisions diff --git a/skills/mxcp-expert/references/project-selection-guide.md b/skills/mxcp-expert/references/project-selection-guide.md new file mode 100644 index 0000000..c0bdc82 --- /dev/null +++ b/skills/mxcp-expert/references/project-selection-guide.md @@ -0,0 +1,929 @@ +# Project Selection Guide + +Decision tree and heuristics for selecting the right MXCP approach and templates based on **technical requirements**. + +**Scope**: This guide helps select implementation patterns (SQL vs Python, template selection, architecture patterns) based on data sources, authentication mechanisms, and technical constraints. It does NOT help define business requirements or determine what features to build. + +## Decision Tree + +Use this decision tree to determine the appropriate MXCP implementation approach: + +``` +User Request + ├─ Data File + │ ├─ CSV file + │ │ ├─ Static data → dbt seed + SQL tool + │ │ ├─ Needs transformation → dbt seed + dbt model + SQL tool + │ │ └─ Large file (>100MB) → Convert to Parquet + dbt model + │ ├─ Excel file (.xlsx, .xls) + │ │ ├─ Static/one-time → Convert to CSV + dbt seed + │ │ ├─ User upload (dynamic) → Python tool with pandas + DuckDB table + │ │ └─ Multi-sheet → Python tool to load all sheets as tables + │ ├─ JSON/Parquet + │ │ └─ DuckDB read_json/read_parquet directly in SQL tool + │ └─ Synthetic data needed + │ ├─ For testing → dbt model with GENERATE_SERIES + │ ├─ Dynamic generation → Python tool with parameters + │ └─ With statistics → Generate + analyze in single tool + │ + ├─ External API Integration + │ ├─ OAuth required + │ │ ├─ Google (Calendar, Sheets, etc.) → google-calendar template + │ │ ├─ Jira Cloud → jira-oauth template + │ │ ├─ Salesforce → salesforce-oauth template + │ │ └─ Other OAuth → Adapt google-calendar template + │ │ + │ ├─ API Token/Basic Auth + │ │ ├─ Jira → jira template + │ │ ├─ Confluence → confluence template + │ │ ├─ Salesforce → salesforce template + │ │ ├─ Custom API → python-demo template + │ │ └─ REST API → Create new Python tool + │ │ + │ └─ Public API (no auth) + │ └─ Create SQL tool with read_json/read_csv from URL + │ + ├─ Database Connection + │ ├─ PostgreSQL + │ │ ├─ Direct query → DuckDB ATTACH + SQL tools + │ │ └─ Cache data → dbt source + model + SQL tools + │ ├─ MySQL + │ │ ├─ Direct query → DuckDB ATTACH + SQL tools + │ │ └─ Cache data → dbt source + model + │ ├─ SQLite → DuckDB ATTACH + SQL tools (simple) + │ ├─ SQL Server → DuckDB ATTACH + SQL tools + │ └─ Other/NoSQL → Create Python tool with connection library + │ + ├─ Complex Logic/Processing + │ ├─ Data transformation → dbt model + │ ├─ Business logic → Python tool + │ ├─ ML/AI processing → Python tool with libraries + │ └─ Async operations → Python tool with async/await + │ + └─ Authentication/Security System + ├─ Keycloak → keycloak template + ├─ Custom SSO → Adapt keycloak template + └─ Policy enforcement → Use MXCP policies +``` + +## Available Project Templates + +### Data-Focused Templates + +#### covid_owid +**Use when**: Working with external data sources, caching datasets + +**Features**: +- dbt integration for data caching +- External CSV/JSON fetching +- Data quality tests +- Incremental updates + +**Example use cases**: +- "Cache COVID statistics for offline analysis" +- "Query external datasets regularly" +- "Download and transform public data" + +**Key files**: +- `models/` - dbt models for data transformation +- `tools/` - SQL tools querying cached data + +#### earthquakes +**Use when**: Real-time data monitoring, geospatial data + +**Features**: +- Real-time API queries +- Geospatial filtering +- Time-based queries + +**Example use cases**: +- "Monitor earthquake activity" +- "Query geospatial data by region" +- "Real-time event tracking" + +### API Integration Templates + +#### google-calendar (OAuth) +**Use when**: Integrating with Google APIs or other OAuth 2.0 services + +**Features**: +- OAuth 2.0 authentication flow +- Token management +- Google API client integration +- Python endpoints with async support + +**Example use cases**: +- "Connect to Google Calendar" +- "Access Google Sheets data" +- "Integrate with Gmail" +- "Any OAuth 2.0 API integration" + +**Adaptation guide**: +1. Replace Google API client with target API client +2. Update OAuth scopes and endpoints +3. Modify tool definitions for new API methods +4. Update configuration with new OAuth provider + +#### jira (API Token) +**Use when**: Integrating with Jira using API tokens + +**Features**: +- API token authentication +- JQL query support +- Issue, user, project management +- Python HTTP client pattern + +**Example use cases**: +- "Query Jira issues" +- "Get project information" +- "Search for users" + +#### jira-oauth (OAuth) +**Use when**: Jira integration requiring OAuth + +**Features**: +- OAuth 1.0a for Jira +- More secure than API tokens +- Full Jira REST API access + +#### confluence +**Use when**: Atlassian Confluence integration + +**Features**: +- Confluence REST API +- Page and space queries +- Content search + +**Example use cases**: +- "Search Confluence pages" +- "Get page content" +- "List spaces" + +#### salesforce / salesforce-oauth +**Use when**: Salesforce CRM integration + +**Features**: +- Salesforce REST API +- SOQL queries +- OAuth or username/password auth + +**Example use cases**: +- "Query Salesforce records" +- "Get account information" +- "Search opportunities" + +### Development Templates + +#### python-demo +**Use when**: Building custom Python-based tools + +**Features**: +- Python endpoint patterns +- Async/await examples +- Database access patterns +- Error handling + +**Example use cases**: +- "Create custom API integration" +- "Implement complex business logic" +- "Build ML/AI-powered tools" + +**Key patterns**: +```python +# Sync endpoint +def simple_tool(param: str) -> dict: + return {"result": param.upper()} + +# Async endpoint +async def async_tool(ids: list[str]) -> list[dict]: + results = await asyncio.gather(*[fetch_data(id) for id in ids]) + return results + +# Database access +def db_tool(query: str) -> list[dict]: + return db.execute(query).fetchall() +``` + +### Infrastructure Templates + +#### plugin +**Use when**: Extending DuckDB with custom functions + +**Features**: +- DuckDB plugin development +- Custom SQL functions +- Compiled extensions + +**Example use cases**: +- "Add custom SQL functions" +- "Integrate C/C++ libraries" +- "Optimize performance-critical operations" + +#### keycloak +**Use when**: Enterprise authentication/authorization + +**Features**: +- Keycloak integration +- SSO support +- Role-based access control + +**Example use cases**: +- "Integrate with Keycloak SSO" +- "Implement role-based policies" +- "Enterprise user management" + +#### squirro +**Use when**: Enterprise search and insights integration + +**Features**: +- Squirro API integration +- Search and analytics +- Enterprise data access + +## Common Scenarios and Heuristics + +### Scenario 1: CSV File to Query + +**User says**: "I need to connect my chat to a CSV file" + +**Heuristic**: +1. **DO NOT** use existing templates +2. **CREATE** new MXCP project from scratch +3. **APPROACH**: + - Place CSV in `seeds/` directory + - Create `seeds/schema.yml` with schema definition and tests + - Run `dbt seed` to load into DuckDB + - Create SQL tool: `SELECT * FROM ` + - Add parameters for filtering if needed + +**Implementation steps**: +```bash +# 1. Initialize project +mkdir csv-server && cd csv-server +mxcp init --bootstrap + +# 2. Setup dbt +mkdir seeds +cp /path/to/file.csv seeds/data.csv + +# 3. Create schema +cat > seeds/schema.yml < tools/query_data.yml < python/api_client.py < dict: + secret = get_secret("api_token") + async with httpx.AsyncClient() as client: + response = await client.get( + f"https://api.example.com/{endpoint}", + headers={"Authorization": f"Bearer {secret['token']}"} + ) + return response.json() +EOF + +# 3. Create tool +# 4. Configure secret in config.yml +# 5. Test +``` + +### Scenario 4: Complex Data Transformation + +**User says**: "Transform this data and provide analytics" + +**Heuristic**: +1. **Use** dbt for transformations +2. **Use** SQL tools for queries +3. **Pattern**: seed → model → tool + +**Implementation steps**: +```bash +# 1. Load source data (seed or external) +# 2. Create dbt model for transformation +cat > models/analytics.sql < seeds/schema.yml # Create schema +dbt seed + +# Option B: Dynamic upload → Python tool +cat > python/excel_loader.py # Create loader +cat > tools/load_excel.yml # Create tool +pip install openpyxl pandas # Add dependencies +``` + +See **references/excel-integration.md** for complete patterns. + +### Scenario 6: Synthetic Data Generation + +**User says**: "Generate test data" or "Create synthetic customer records" or "I need dummy data for testing" + +**Heuristic**: +1. **If persistent test data**: dbt model with GENERATE_SERIES +2. **If dynamic/parameterized**: Python tool +3. **If with analysis**: Generate + calculate statistics in one tool + +**Implementation steps**: +```bash +# Option A: Persistent via dbt +cat > models/synthetic_customers.sql < python/generate_data.py # Create generator +cat > tools/generate_test_data.yml # Create tool +``` + +See **references/synthetic-data-patterns.md** for complete patterns. + +### Scenario 7: Python Library Wrapping + +**User says**: "Wrap the Stripe API" or "Use pandas for analysis" or "Connect to Redis" + +**Heuristic**: +1. **Check** if it's an API client library (stripe, twilio, etc.) +2. **Check** if it's a data/ML library (pandas, sklearn, etc.) +3. **Use** `python-demo` as base +4. **Add** library to requirements.txt +5. **Use** @on_init for initialization if stateful + +**Implementation steps**: +```bash +# 1. Copy python-demo template +cp -r assets/project-templates/python-demo my-project + +# 2. Install library +echo "stripe>=5.4.0" >> requirements.txt +pip install stripe + +# 3. Create wrapper +cat > python/stripe_wrapper.py # Implement wrapper functions + +# 4. Create tools +cat > tools/create_customer.yml # Map to wrapper functions + +# 5. Create project config with secrets +cat > config.yml < python/ml_tool.py < list[dict]: + results = classifier(texts) + return [{"text": t, **r} for t, r in zip(texts, results)] +EOF + +# 4. Create tool definition +# 5. Test +``` + +### Scenario 9: External Database Connection + +**User says**: "Connect to my PostgreSQL database" or "Query my MySQL production database" + +**Heuristic**: +1. **Ask** if data can be exported to CSV (simpler approach) +2. **Ask** if they need real-time data or can cache it +3. **Decide**: Direct query (ATTACH) vs cached (dbt) + +**Implementation steps - Direct Query (ATTACH)**: +```bash +# 1. Create project +mkdir db-connection && cd db-connection +mxcp init --bootstrap + +# 2. Create config with credentials +cat > config.yml < tools/query_database.yml < models/sources.yml < models/customer_cache.sql < models/schema.yml < tools/query_cached.yml <100MB)" +- "How often does the data update? (static, daily, real-time)" + +### Security Requirements Unclear +- "Who should have access to this data? (everyone, specific roles, specific users)" +- "Are there any sensitive fields that need protection?" + +### Functionality Unclear +- "What questions do you want to ask about this data?" +- "What operations should be available through the MCP server?" + +## Heuristics When No Interaction Available + +**If cannot ask questions, use these defaults**: + +1. **CSV file mentioned** → dbt seed + SQL tool with `SELECT *` +2. **Excel mentioned** → Convert to CSV + dbt seed OR Python pandas tool +3. **API mentioned** → Check for template, otherwise use Python tool with httpx +4. **OAuth mentioned** → Use google-calendar template as base +5. **Database mentioned** → DuckDB ATTACH for direct query OR dbt for caching +6. **PostgreSQL/MySQL mentioned** → Use ATTACH with read-only user +7. **Transformation needed** → dbt model +8. **Complex logic** → Python tool +9. **Security not mentioned** → No policies (user can add later) +10. **No auth mentioned for API** → Assume token/basic auth + +## Configuration Management + +### Project-Local Config (Recommended) + +**ALWAYS create `config.yml` in the project directory, NOT `~/.mxcp/config.yml`** + +**Why?** +- User maintains control over global config +- Project is self-contained and portable +- Safer for agents (no global config modification) +- User can review before copying to ~/.mxcp/ + +**Basic config.yml template**: +```yaml +# config.yml (in project root) +mxcp: 1 + +profiles: + default: + # Secrets via environment variables (recommended) + secrets: + - name: api_token + type: env + parameters: + env_var: API_TOKEN + + # Database configuration (optional, default is data/db-default.duckdb) + database: + path: "data/db-default.duckdb" + + # Authentication (if needed) + auth: + provider: github # or google, microsoft, etc. + + production: + database: + path: "prod.duckdb" + audit: + enabled: true + path: "audit.jsonl" +``` + +**Usage options**: +```bash +# Option 1: Auto-discover (mxcp looks for ./config.yml) +mxcp serve + +# Option 2: Explicit path via environment variable +MXCP_CONFIG=./config.yml mxcp serve + +# Option 3: User manually copies to global location +cp config.yml ~/.mxcp/config.yml +mxcp serve +``` + +**In skill implementations**: +```bash +# CORRECT: Create local config +cat > config.yml <` +- [ ] Test with invalid inputs +- [ ] Test with edge cases (empty data, nulls, etc.) + +## Summary + +**Quick reference for common requests**: + +| User Request | Approach | Template | Key Steps | +|--------------|----------|----------|-----------| +| "Query my CSV" | dbt seed + SQL tool | None | seed → schema.yml → dbt seed/test → SQL tool | +| "Read Excel file" | Convert to CSV + dbt seed OR pandas tool | None | Excel→CSV → seed OR pandas → DuckDB table | +| "Connect to PostgreSQL" | ATTACH + SQL tool OR dbt cache | None | ATTACH → SQL tool OR dbt source/model → SQL tool | +| "Connect to MySQL" | ATTACH + SQL tool OR dbt cache | None | ATTACH → SQL tool OR dbt source/model → SQL tool | +| "Generate test data" | dbt model or Python | None | GENERATE_SERIES → dbt model or Python tool | +| "Wrap library X" | Python wrapper | python-demo | Install lib → wrap functions → create tools | +| "Connect to Google Calendar" | OAuth + Python | google-calendar | Copy template → configure OAuth | +| "Connect to Jira" | Token + Python | jira or jira-oauth | Copy template → configure token | +| "Transform data" | dbt model | None | seed/source → model → schema.yml → dbt run/test → SQL tool | +| "Complex logic" | Python tool | python-demo | Copy template → implement function | +| "ML/AI task" | Python + libraries | python-demo | Add ML libs → implement model | +| "External API" | Python + httpx | python-demo | Implement client → create tool | + +**Priority order**: +1. Security (auth, policies, validation) +2. Robustness (error handling, types, tests) +3. Testing (validate, test, lint) +4. Features (based on user needs) diff --git a/skills/mxcp-expert/references/python-api.md b/skills/mxcp-expert/references/python-api.md new file mode 100644 index 0000000..7b99ae7 --- /dev/null +++ b/skills/mxcp-expert/references/python-api.md @@ -0,0 +1,830 @@ +# Python Runtime API Reference + +Complete reference for MXCP Python endpoints, including wrapping external libraries and packages. + +## Database Access + +```python +from mxcp.runtime import db + +# Execute query +results = db.execute( + "SELECT * FROM users WHERE id = $id", + {"id": user_id} +) + +# Get first result +first = results[0] if results else None + +# Iterate results +for row in results: + print(row["name"]) +``` + +**Important**: Always access through `db.execute()`, never cache `db.connection`. + +## Configuration & Secrets + +```python +from mxcp.runtime import config + +# Get secret (returns dict with parameters) +secret = config.get_secret("api_key") +api_key = secret["value"] if secret else None + +# For complex secrets (like HTTP with headers) +http_secret = config.get_secret("api_service") +if http_secret: + token = http_secret.get("BEARER_TOKEN") + headers = http_secret.get("EXTRA_HTTP_HEADERS", {}) + +# Get settings +project_name = config.get_setting("project") +debug_mode = config.get_setting("debug", default=False) + +# Access full configs +user_config = config.user_config +site_config = config.site_config +``` + +## Lifecycle Hooks + +```python +from mxcp.runtime import on_init, on_shutdown +import httpx + +client = None + +@on_init +def setup(): + """Initialize resources at startup""" + global client + client = httpx.Client() + print("Client initialized") + +@on_shutdown +def cleanup(): + """Clean up resources at shutdown""" + global client + if client: + client.close() +``` + +**IMPORTANT: Lifecycle hooks are for Python resources ONLY** + +- ✅ **USE FOR**: HTTP clients, external API connections, ML model loading, cache clients +- ❌ **DON'T USE FOR**: Database management, DuckDB connections, dbt operations + +The DuckDB connection is managed automatically by MXCP. These hooks are for managing Python-specific resources that need initialization at server startup and cleanup at shutdown. + +## Async Functions + +```python +import asyncio +import aiohttp + +async def fetch_data(urls: list[str]) -> list[dict]: + """Fetch from multiple URLs concurrently""" + + async def fetch_one(url: str) -> dict: + async with aiohttp.ClientSession() as session: + async with session.get(url) as response: + return await response.json() + + results = await asyncio.gather(*[fetch_one(url) for url in urls]) + return results +``` + +## Return Types + +Match your function return to the endpoint's return type: + +```python +# Array return +def list_items() -> list: + return [{"id": 1}, {"id": 2}] + +# Object return +def get_stats() -> dict: + return {"total": 100, "active": 75} + +# Scalar return +def count_items() -> int: + return 42 +``` + +## Shared Modules + +Organize code in subdirectories: + +```python +# python/utils/validators.py +def validate_email(email: str) -> bool: + import re + return bool(re.match(r'^[\w\.-]+@[\w\.-]+\.\w+$', email)) + +# python/main_tool.py +from utils.validators import validate_email + +def process_user(email: str) -> dict: + if not validate_email(email): + return {"error": "Invalid email"} + return {"status": "ok"} +``` + +## Error Handling + +```python +def safe_divide(a: float, b: float) -> dict: + if b == 0: + return {"error": "Division by zero"} + return {"result": a / b} +``` + +## External API Integration Pattern + +```python +import httpx +from mxcp.runtime import config, db + +async def call_external_api(param: str) -> dict: + # Get API key + api_key = config.get_secret("external_api")["value"] + + # Check cache + cached = db.execute( + "SELECT data FROM cache WHERE key = $key AND ts > datetime('now', '-1 hour')", + {"key": param} + ).fetchone() + + if cached: + return cached["data"] + + # Make API call + async with httpx.AsyncClient() as client: + response = await client.get( + "https://api.example.com/data", + params={"q": param, "key": api_key} + ) + data = response.json() + + # Cache result + db.execute( + "INSERT OR REPLACE INTO cache (key, data, ts) VALUES ($1, $2, CURRENT_TIMESTAMP)", + {"key": param, "data": data} + ) + + return data +``` + +## Database Reload (Advanced) + +Use `reload_duckdb` only when external tools need exclusive database access: + +```python +from mxcp.runtime import reload_duckdb + +def rebuild_database(): + """Trigger database rebuild""" + + def rebuild(): + # Run with exclusive database access + import subprocess + subprocess.run(["dbt", "run"], check=True) + + reload_duckdb( + payload_func=rebuild, + description="Rebuilding with dbt" + ) + + return {"status": "Reload scheduled"} +``` + +**Note**: Normally you don't need this. Use `db.execute()` for direct operations. + +## Wrapping External Libraries + +### Pattern 1: Simple Library Wrapper + +**Use case**: Expose existing Python library as MCP tool + +```python +# python/library_wrapper.py +"""Wrapper for an existing library like requests, pandas, etc.""" + +import requests +from mxcp.runtime import get_secret + +def fetch_url(url: str, method: str = "GET", headers: dict = None) -> dict: + """Wrap requests library as MCP tool""" + try: + # Get auth if needed + secret = get_secret("api_token") + if secret and headers is None: + headers = {"Authorization": f"Bearer {secret['token']}"} + + response = requests.request(method, url, headers=headers, timeout=30) + response.raise_for_status() + + return { + "status_code": response.status_code, + "headers": dict(response.headers), + "body": response.json() if response.headers.get('content-type', '').startswith('application/json') else response.text + } + except requests.RequestException as e: + return {"error": str(e), "status": "failed"} +``` + +```yaml +# tools/http_request.yml +mxcp: 1 +tool: + name: http_request + description: "Make HTTP requests using requests library" + language: python + parameters: + - name: url + type: string + - name: method + type: string + default: "GET" + return: + type: object + source: + file: ../python/library_wrapper.py +``` + +### Pattern 2: Data Science Library Wrapper (pandas, numpy) + +```python +# python/data_analysis.py +"""Wrap pandas for data analysis""" + +import pandas as pd +import numpy as np +from mxcp.runtime import db + +def analyze_dataframe(table_name: str) -> dict: + """Analyze a table using pandas""" + # Read from DuckDB into pandas + df = db.execute(f"SELECT * FROM {table_name}").df() + + # Pandas analysis + analysis = { + "shape": df.shape, + "columns": list(df.columns), + "dtypes": df.dtypes.astype(str).to_dict(), + "missing_values": df.isnull().sum().to_dict(), + "summary_stats": df.describe().to_dict(), + "memory_usage": df.memory_usage(deep=True).sum() + } + + # Numeric column correlations + numeric_cols = df.select_dtypes(include=[np.number]).columns + if len(numeric_cols) > 1: + analysis["correlations"] = df[numeric_cols].corr().to_dict() + + return analysis + +def pandas_query(table_name: str, operation: str) -> dict: + """Execute pandas operations on DuckDB table""" + df = db.execute(f"SELECT * FROM {table_name}").df() + + # Support common pandas operations + if operation == "describe": + result = df.describe().to_dict() + elif operation == "head": + result = df.head(10).to_dict('records') + elif operation == "value_counts": + # For first categorical column + cat_col = df.select_dtypes(include=['object']).columns[0] + result = df[cat_col].value_counts().to_dict() + else: + return {"error": f"Unknown operation: {operation}"} + + return {"operation": operation, "result": result} +``` + +### Pattern 3: ML Library Wrapper (scikit-learn) + +```python +# python/ml_wrapper.py +"""Wrap scikit-learn for ML tasks""" + +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from mxcp.runtime import db, on_init +import pickle +import os + +# Global model store +models = {} + +@on_init +def load_models(): + """Load saved models on startup""" + global models + model_dir = "models" + if os.path.exists(model_dir): + for file in os.listdir(model_dir): + if file.endswith('.pkl'): + model_name = file[:-4] + with open(os.path.join(model_dir, file), 'rb') as f: + models[model_name] = pickle.load(f) + +def train_classifier( + table_name: str, + target_column: str, + feature_columns: list[str], + model_name: str = "default" +) -> dict: + """Train a classifier on DuckDB table""" + # Load data + df = db.execute(f"SELECT * FROM {table_name}").df() + + X = df[feature_columns] + y = df[target_column] + + # Split data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Train model + model = RandomForestClassifier(n_estimators=100, random_state=42) + model.fit(X_train, y_train) + + # Evaluate + train_score = model.score(X_train, y_train) + test_score = model.score(X_test, y_test) + + # Save model + global models + models[model_name] = model + os.makedirs("models", exist_ok=True) + with open(f"models/{model_name}.pkl", 'wb') as f: + pickle.dump(model, f) + + return { + "model_name": model_name, + "train_accuracy": train_score, + "test_accuracy": test_score, + "feature_importance": dict(zip(feature_columns, model.feature_importances_)) + } + +def predict(model_name: str, features: dict) -> dict: + """Make prediction with trained model""" + if model_name not in models: + return {"error": f"Model '{model_name}' not found"} + + model = models[model_name] + + # Convert features to DataFrame with correct order + import pandas as pd + feature_df = pd.DataFrame([features]) + + prediction = model.predict(feature_df)[0] + probabilities = model.predict_proba(feature_df)[0] if hasattr(model, 'predict_proba') else None + + return { + "prediction": prediction, + "probabilities": probabilities.tolist() if probabilities is not None else None + } +``` + +### Pattern 4: API Client Library Wrapper + +```python +# python/api_client_wrapper.py +"""Wrap an API client library (e.g., stripe, twilio, sendgrid)""" + +import stripe +from mxcp.runtime import get_secret, on_init + +@on_init +def initialize_stripe(): + """Configure Stripe on startup""" + secret = get_secret("stripe") + if secret: + stripe.api_key = secret["api_key"] + +def create_customer(email: str, name: str) -> dict: + """Wrap Stripe customer creation""" + try: + customer = stripe.Customer.create( + email=email, + name=name + ) + return { + "customer_id": customer.id, + "email": customer.email, + "name": customer.name, + "created": customer.created + } + except stripe.error.StripeError as e: + return {"error": str(e), "type": e.__class__.__name__} + +def list_charges(customer_id: str = None, limit: int = 10) -> dict: + """Wrap Stripe charges listing""" + try: + charges = stripe.Charge.list( + customer=customer_id, + limit=limit + ) + return { + "charges": [ + { + "id": charge.id, + "amount": charge.amount, + "currency": charge.currency, + "status": charge.status, + "created": charge.created + } + for charge in charges.data + ] + } + except stripe.error.StripeError as e: + return {"error": str(e)} +``` + +### Pattern 5: Async Library Wrapper + +```python +# python/async_library_wrapper.py +"""Wrap async libraries like httpx, aiohttp""" + +import httpx +import asyncio +from mxcp.runtime import get_secret + +async def batch_fetch(urls: list[str]) -> list[dict]: + """Fetch multiple URLs concurrently""" + async with httpx.AsyncClient(timeout=30.0) as client: + async def fetch_one(url: str) -> dict: + try: + response = await client.get(url) + return { + "url": url, + "status": response.status_code, + "data": response.json() if response.headers.get('content-type', '').startswith('application/json') else response.text + } + except Exception as e: + return {"url": url, "error": str(e)} + + results = await asyncio.gather(*[fetch_one(url) for url in urls]) + return results + +async def graphql_query(endpoint: str, query: str, variables: dict = None) -> dict: + """Wrap GraphQL library/client""" + secret = get_secret("graphql_api") + headers = {"Authorization": f"Bearer {secret['token']}"} if secret else {} + + async with httpx.AsyncClient() as client: + response = await client.post( + endpoint, + json={"query": query, "variables": variables or {}}, + headers=headers + ) + return response.json() +``` + +### Pattern 6: Complex Library with State Management + +```python +# python/stateful_library_wrapper.py +"""Wrap libraries that maintain state (e.g., database connections, cache clients)""" + +from redis import Redis +from mxcp.runtime import get_secret, on_init, on_shutdown + +redis_client = None + +@on_init +def connect_redis(): + """Initialize Redis connection on startup""" + global redis_client + secret = get_secret("redis") + if secret: + redis_client = Redis( + host=secret["host"], + port=secret.get("port", 6379), + password=secret.get("password"), + decode_responses=True + ) + +@on_shutdown +def disconnect_redis(): + """Clean up Redis connection""" + global redis_client + if redis_client: + redis_client.close() + +def cache_set(key: str, value: str, ttl: int = 3600) -> dict: + """Set value in Redis cache""" + if not redis_client: + return {"error": "Redis not configured"} + + try: + redis_client.setex(key, ttl, value) + return {"status": "success", "key": key, "ttl": ttl} + except Exception as e: + return {"error": str(e)} + +def cache_get(key: str) -> dict: + """Get value from Redis cache""" + if not redis_client: + return {"error": "Redis not configured"} + + try: + value = redis_client.get(key) + return {"key": key, "value": value, "found": value is not None} + except Exception as e: + return {"error": str(e)} +``` + +## Dependency Management + +### requirements.txt + +Always include dependencies for wrapped libraries: + +```txt +# requirements.txt + +# HTTP clients +requests>=2.31.0 +httpx>=0.24.0 +aiohttp>=3.8.0 + +# Data processing +pandas>=2.0.0 +numpy>=1.24.0 +openpyxl>=3.1.0 # For Excel support + +# ML libraries +scikit-learn>=1.3.0 + +# API clients +stripe>=5.4.0 +twilio>=8.0.0 +sendgrid>=6.10.0 + +# Database/Cache +redis>=4.5.0 +psycopg2-binary>=2.9.0 # For PostgreSQL + +# Other common libraries +pillow>=10.0.0 # Image processing +beautifulsoup4>=4.12.0 # HTML parsing +lxml>=4.9.0 # XML parsing +``` + +### Installing Dependencies + +```bash +# In project directory +pip install -r requirements.txt + +# Or install specific library +pip install pandas requests +``` + +## Error Handling for Library Wrappers + +**Always handle library-specific exceptions**: + +```python +def safe_library_call(param: str) -> dict: + """Template for safe library wrapping""" + try: + # Import library (can fail if not installed) + import some_library + + # Use library + result = some_library.do_something(param) + + return {"success": True, "result": result} + + except ImportError as e: + return { + "error": "Library not installed", + "message": str(e), + "fix": "Run: pip install some_library" + } + except some_library.SpecificError as e: + return { + "error": "Library-specific error", + "message": str(e), + "type": e.__class__.__name__ + } + except Exception as e: + return { + "error": "Unexpected error", + "message": str(e), + "type": e.__class__.__name__ + } +``` + +## Database Reload (Advanced) + +**Important**: In most cases, you DON'T need this feature. Use `db.execute()` directly for database operations. + +The `reload_duckdb()` function allows Python endpoints to trigger a safe reload of the DuckDB database. This is **only** needed when external processes require exclusive access to the database file. + +### When to Use + +Use `reload_duckdb()` ONLY when: +- External tools need exclusive database access (e.g., running `dbt` as a subprocess) +- You're replacing the entire database file +- External processes cannot operate within the same Python process + +### When NOT to Use + +- ❌ Regular database operations (use `db.execute()` instead) +- ❌ Running dbt (use dbt Python API directly in the same process) +- ❌ Loading data from APIs/files (use `db.execute()` to insert data) + +DuckDB's concurrency model allows the MXCP process to own the connection while multiple threads operate safely. Only use `reload_duckdb()` if you absolutely must have an external process update the database file. + +### API + +```python +from mxcp.runtime import reload_duckdb + +def update_data_endpoint() -> dict: + """Endpoint that triggers a data refresh""" + + def rebuild_database(): + """ + This function runs with all connections closed. + You have exclusive access to the DuckDB file. + """ + # Example: Run external tool + import subprocess + subprocess.run(["dbt", "run", "--target", "prod"], check=True) + + # Or: Replace with pre-built database + import shutil + shutil.copy("/staging/analytics.duckdb", "/app/data/analytics.duckdb") + + # Or: Load fresh data + import pandas as pd + import duckdb + df = pd.read_parquet("s3://bucket/latest-data.parquet") + conn = duckdb.connect("/app/data/analytics.duckdb") + conn.execute("CREATE OR REPLACE TABLE sales AS SELECT * FROM df") + conn.close() + + # Schedule the reload (happens asynchronously) + reload_duckdb( + payload_func=rebuild_database, + description="Updating analytics data" + ) + + # Return immediately - reload happens in background + return { + "status": "scheduled", + "message": "Data refresh will complete in background" + } +``` + +### How It Works + +When you call `reload_duckdb()`: + +1. **Queues the reload** - Function returns immediately to client +2. **Drains active requests** - Existing requests complete normally +3. **Shuts down runtime** - Closes Python hooks and DuckDB connections +4. **Runs your payload** - With all connections closed and exclusive access +5. **Restarts runtime** - Fresh configuration and connections +6. **Processes waiting requests** - With the updated data + +### Real-World Example + +```python +from mxcp.runtime import reload_duckdb, db +from datetime import datetime +import requests + +def scheduled_update(source: str = "api") -> dict: + """Endpoint called by cron to update data""" + + def rebuild_from_api(): + # Fetch data from external API + response = requests.get("https://api.example.com/analytics/export") + data = response.json() + + # Write to DuckDB (exclusive access guaranteed) + import duckdb + conn = duckdb.connect("/app/data/analytics.duckdb") + + # Clear old data + conn.execute("DROP TABLE IF EXISTS daily_metrics") + + # Load new data + conn.execute(""" + CREATE TABLE daily_metrics AS + SELECT * FROM read_json_auto(?) + """, [data]) + + # Update metadata + conn.execute(""" + INSERT INTO update_log (timestamp, source, record_count) + VALUES (?, ?, ?) + """, [datetime.now(), source, len(data)]) + + conn.close() + + reload_duckdb( + payload_func=rebuild_from_api, + description=f"Scheduled update from {source}" + ) + + return { + "status": "scheduled", + "source": source, + "timestamp": datetime.now().isoformat() + } +``` + +### Best Practices + +1. **Avoid when possible** - Prefer direct `db.execute()` operations +2. **Return immediately** - Don't wait for reload in your endpoint +3. **Handle errors in payload** - Wrap payload logic in try/except +4. **Keep payload fast** - Long-running payloads block new requests +5. **Document behavior** - Let users know data refresh is asynchronous + +## Plugin System + +MXCP supports a plugin system for extending DuckDB with custom Python functions. + +### Accessing Plugins + +```python +from mxcp.runtime import plugins + +# Get a specific plugin +my_plugin = plugins.get("my_custom_plugin") +if my_plugin: + result = my_plugin.some_method() + +# List available plugins +available_plugins = plugins.list() +print(f"Available plugins: {available_plugins}") +``` + +### Example Usage + +```python +def use_custom_function(data: str) -> dict: + """Use a custom DuckDB function from a plugin""" + + # Get the plugin + text_plugin = plugins.get("text_processing") + if not text_plugin: + return {"error": "text_processing plugin not available"} + + # Use plugin functionality + result = text_plugin.normalize_text(data) + + return {"normalized": result} +``` + +### Plugin Definition + +Plugins are defined in `plugins/` directory: + +```python +# plugins/my_plugin.py +def custom_transform(value: str) -> str: + """Custom transformation logic""" + return value.upper() + +# Register with DuckDB if needed +def register_functions(conn): + """Register custom functions with DuckDB""" + conn.create_function("custom_upper", custom_transform) +``` + +See official MXCP documentation for complete plugin development guide. + +## Best Practices for Library Wrapping + +1. **Initialize once**: Use `@on_init` for expensive setup (connections, model loading) +2. **Clean up**: Use `@on_shutdown` to release resources (HTTP clients, NOT database) +3. **Handle errors**: Catch library-specific exceptions, return error dicts +4. **Document dependencies**: List in requirements.txt with versions +5. **Type hints**: Add for better IDE support and documentation +6. **Async when appropriate**: Use async for I/O-bound library operations +7. **State management**: Use global variables + lifecycle hooks for stateful clients +8. **Version pin**: Pin library versions to avoid breaking changes +9. **Timeout handling**: Add timeouts for network operations +10. **Return simple types**: Convert library-specific objects to dicts/lists + +## General Best Practices + +1. **Database Access**: Always use `db.execute()`, never cache connections +2. **Error Handling**: Return error dicts instead of raising exceptions +3. **Type Hints**: Use for better IDE support +4. **Logging**: Use standard Python logging +5. **Resource Management**: Use context managers +6. **Async**: Use for I/O-bound operations diff --git a/skills/mxcp-expert/references/python-development-workflow.md b/skills/mxcp-expert/references/python-development-workflow.md new file mode 100644 index 0000000..e113200 --- /dev/null +++ b/skills/mxcp-expert/references/python-development-workflow.md @@ -0,0 +1,516 @@ +# Python Development Workflow for MXCP + +**Complete guide for Python development in MXCP projects using uv, black, pyright, and pytest.** + +## Overview + +MXCP Python development requires specific tooling to ensure code quality, type safety, and testability. This guide covers the complete workflow from project setup to deployment. + +## Required Tools + +### uv - Fast Python Package Manager +**Why**: Faster than pip, better dependency resolution, virtual environment management +**Install**: `curl -LsSf https://astral.sh/uv/install.sh | sh` + +### black - Code Formatter +**Why**: Consistent code style, zero configuration +**Install**: Via uv (see below) + +### pyright - Type Checker +**Why**: Catch type errors before runtime, better IDE support +**Install**: Via uv (see below) + +### pytest - Testing Framework +**Why**: Simple, powerful, async support, mocking capabilities +**Install**: Via uv (see below) + +## Complete Workflow + +### Phase 1: Project Initialization + +```bash +# 1. Create project directory +mkdir my-mxcp-server +cd my-mxcp-server + +# 2. Create virtual environment with uv +uv venv + +# Output: +# Using CPython 3.11.x interpreter at: /usr/bin/python3 +# Creating virtual environment at: .venv +# Activate with: source .venv/bin/activate + +# 3. Activate virtual environment +source .venv/bin/activate + +# Verify activation (prompt should show (.venv)) +which python +# Output: /path/to/my-mxcp-server/.venv/bin/python + +# 4. Install MXCP and development tools +uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov + +# 5. Initialize MXCP project +mxcp init --bootstrap + +# 6. Create requirements.txt for reproducibility +cat > requirements.txt <<'EOF' +mxcp>=0.1.0 +black>=24.0.0 +pyright>=1.1.0 +pytest>=7.0.0 +pytest-asyncio>=0.21.0 +pytest-httpx>=0.21.0 +pytest-cov>=4.0.0 +EOF +``` + +### Phase 2: Writing Python Code + +**CRITICAL: Always activate virtual environment before any work.** + +```bash +# Check if virtual environment is active +echo $VIRTUAL_ENV +# Should show: /path/to/your/project/.venv + +# If not active, activate it +source .venv/bin/activate +``` + +#### Create Python Tool + +```bash +# Create Python module +cat > python/customer_tools.py <<'EOF' +"""Customer management tools.""" + +from mxcp.runtime import db +from typing import Dict, List, Optional + + +async def get_customer_summary(customer_id: str) -> Dict[str, any]: + """ + Get comprehensive customer summary. + + Args: + customer_id: Customer identifier + + Returns: + Customer summary with orders and spending info + """ + # Get customer data + customer = db.execute( + "SELECT * FROM customers WHERE id = $id", + {"id": customer_id} + ).fetchone() + + if not customer: + return { + "success": False, + "error": f"Customer {customer_id} not found", + "error_code": "NOT_FOUND", + } + + # Get order summary + orders = db.execute( + """ + SELECT + COUNT(*) as order_count, + COALESCE(SUM(total), 0) as total_spent + FROM orders + WHERE customer_id = $id + """, + {"id": customer_id} + ).fetchone() + + return { + "success": True, + "customer_id": customer["id"], + "name": customer["name"], + "email": customer["email"], + "order_count": orders["order_count"], + "total_spent": float(orders["total_spent"]), + } +EOF +``` + +#### Format Code with Black + +**ALWAYS run after creating or editing Python files:** + +```bash +# Format specific directory +black python/ + +# Output: +# reformatted python/customer_tools.py +# All done! ✨ 🍰 ✨ +# 1 file reformatted. + +# Format specific file +black python/customer_tools.py + +# Check what would be formatted (dry-run) +black --check python/ + +# See diff of changes +black --diff python/ +``` + +**Black configuration** (optional): +```toml +# pyproject.toml +[tool.black] +line-length = 100 +target-version = ['py311'] +``` + +#### Run Type Checker + +**ALWAYS run after creating or editing Python files:** + +```bash +# Check all Python files +pyright python/ + +# Output if types are correct: +# 0 errors, 0 warnings, 0 informations + +# Output if there are issues: +# python/customer_tools.py:15:12 - error: Type of "any" is unknown +# 1 error, 0 warnings, 0 informations + +# Check specific file +pyright python/customer_tools.py + +# Check with verbose output +pyright --verbose python/ +``` + +**Fix common type issues**: + +```python +# ❌ WRONG: Using 'any' type +async def get_customer_summary(customer_id: str) -> Dict[str, any]: + pass + +# ✅ CORRECT: Use proper types +from typing import Dict, Any, Union + +async def get_customer_summary(customer_id: str) -> Dict[str, Union[str, int, float, bool]]: + pass + +# ✅ BETTER: Define response type +from typing import TypedDict + +class CustomerSummary(TypedDict): + success: bool + customer_id: str + name: str + email: str + order_count: int + total_spent: float + +async def get_customer_summary(customer_id: str) -> CustomerSummary: + pass +``` + +**Pyright configuration** (optional): +```json +// pyrightconfig.json +{ + "include": ["python"], + "exclude": [".venv", "**/__pycache__"], + "typeCheckingMode": "strict", + "reportMissingTypeStubs": false +} +``` + +### Phase 3: Writing Tests + +**Create tests in `tests/` directory:** + +```bash +# Create test directory structure +mkdir -p tests +touch tests/__init__.py + +# Create test file +cat > tests/test_customer_tools.py <<'EOF' +"""Tests for customer_tools module.""" + +import pytest +from python.customer_tools import get_customer_summary +from unittest.mock import Mock, patch + + +@pytest.mark.asyncio +async def test_get_customer_summary_success(): + """Test successful customer summary retrieval.""" + # Mock database responses + with patch("python.customer_tools.db") as mock_db: + # Mock customer query + mock_db.execute.return_value.fetchone.side_effect = [ + {"id": "CUST_123", "name": "John Doe", "email": "john@example.com"}, + {"order_count": 5, "total_spent": 1000.50} + ] + + result = await get_customer_summary("CUST_123") + + assert result["success"] is True + assert result["customer_id"] == "CUST_123" + assert result["name"] == "John Doe" + assert result["order_count"] == 5 + assert result["total_spent"] == 1000.50 + + +@pytest.mark.asyncio +async def test_get_customer_summary_not_found(): + """Test customer not found error handling.""" + with patch("python.customer_tools.db") as mock_db: + mock_db.execute.return_value.fetchone.return_value = None + + result = await get_customer_summary("CUST_999") + + assert result["success"] is False + assert result["error_code"] == "NOT_FOUND" + assert "CUST_999" in result["error"] +EOF +``` + +#### Run Tests + +```bash +# Run all tests with verbose output +pytest tests/ -v + +# Output: +# tests/test_customer_tools.py::test_get_customer_summary_success PASSED +# tests/test_customer_tools.py::test_get_customer_summary_not_found PASSED +# ======================== 2 passed in 0.15s ======================== + +# Run with coverage +pytest tests/ --cov=python --cov-report=term-missing + +# Output: +# Name Stmts Miss Cover Missing +# ------------------------------------------------------------ +# python/customer_tools.py 25 0 100% +# ------------------------------------------------------------ +# TOTAL 25 0 100% + +# Run specific test +pytest tests/test_customer_tools.py::test_get_customer_summary_success -v + +# Run with output capture disabled (see prints) +pytest tests/ -v -s +``` + +### Phase 4: Complete Code Edit Cycle + +**MANDATORY workflow after every Python code edit:** + +```bash +# 1. Ensure virtual environment is active +source .venv/bin/activate + +# 2. Format code +black python/ +# Must see: "All done! ✨ 🍰 ✨" + +# 3. Type check +pyright python/ +# Must see: "0 errors, 0 warnings, 0 informations" + +# 4. Run tests +pytest tests/ -v +# Must see: All tests PASSED + +# 5. Only after ALL pass, proceed with next step +``` + +**If any check fails, fix before proceeding!** + +### Phase 5: MXCP Validation and Testing + +```bash +# Ensure virtual environment is active +source .venv/bin/activate + +# 1. Validate structure +mxcp validate + +# 2. Run MXCP integration tests +mxcp test + +# 3. Run manual test +mxcp run tool get_customer_summary --param customer_id=CUST_123 + +# 4. Check documentation quality +mxcp lint +``` + +## Complete Checklist + +Before declaring Python code complete: + +### Setup Checklist +- [ ] Virtual environment created: `uv venv` +- [ ] Virtual environment activated: `source .venv/bin/activate` +- [ ] Dependencies installed: `uv pip install mxcp black pyright pytest pytest-asyncio pytest-httpx pytest-cov` +- [ ] `requirements.txt` created with all dependencies + +### Code Quality Checklist +- [ ] Code formatted: `black python/` shows "All done!" +- [ ] Type checking passes: `pyright python/` shows "0 errors" +- [ ] All functions have type hints +- [ ] All functions have docstrings +- [ ] Error handling returns structured dicts + +### Testing Checklist +- [ ] Unit tests created in `tests/` +- [ ] All tests pass: `pytest tests/ -v` +- [ ] External calls are mocked +- [ ] Test coverage >80%: `pytest --cov=python tests/` +- [ ] Result correctness verified (not just structure) +- [ ] Concurrency safety verified (if stateful) + +### MXCP Checklist +- [ ] MXCP validation passes: `mxcp validate` +- [ ] MXCP tests pass: `mxcp test` +- [ ] Manual test succeeds: `mxcp run tool ` +- [ ] Documentation complete: `mxcp lint` passes + +## Common Issues and Solutions + +### Issue 1: Virtual Environment Not Active + +**Symptom**: Commands not found or using wrong Python + +```bash +# Check if active +which python +# Should show: /path/to/project/.venv/bin/python + +# If not, activate +source .venv/bin/activate +``` + +### Issue 2: Black Formatting Fails + +**Symptom**: Syntax errors in Python code + +```bash +# Fix syntax errors first +python -m py_compile python/your_file.py + +# Then format +black python/ +``` + +### Issue 3: Pyright Type Errors + +**Symptom**: "Type of X is unknown" + +```python +# Add type hints +from typing import Dict, List, Optional, Any + +# Use proper return types +def my_function() -> Dict[str, Any]: + return {"key": "value"} +``` + +### Issue 4: Pytest Import Errors + +**Symptom**: "ModuleNotFoundError: No module named 'python'" + +```bash +# Ensure you're running from project root +pwd # Should show project directory + +# Ensure virtual environment is active +source .venv/bin/activate + +# Run pytest from project root +pytest tests/ -v +``` + +### Issue 5: MXCP Commands Not Found + +**Symptom**: "command not found: mxcp" + +```bash +# Virtual environment not active +source .venv/bin/activate + +# Verify mxcp is installed +which mxcp +# Should show: /path/to/project/.venv/bin/mxcp +``` + +## Integration with CI/CD + +```yaml +# .github/workflows/test.yml +name: Test + +on: [push, pull_request] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Install uv + run: curl -LsSf https://astral.sh/uv/install.sh | sh + + - name: Create virtual environment + run: uv venv + + - name: Install dependencies + run: | + source .venv/bin/activate + uv pip install -r requirements.txt + + - name: Format check + run: | + source .venv/bin/activate + black --check python/ + + - name: Type check + run: | + source .venv/bin/activate + pyright python/ + + - name: Run unit tests + run: | + source .venv/bin/activate + pytest tests/ -v --cov=python --cov-report=xml + + - name: MXCP validate + run: | + source .venv/bin/activate + mxcp validate + + - name: MXCP test + run: | + source .venv/bin/activate + mxcp test +``` + +## Summary + +**Python development workflow for MXCP**: + +1. ✅ Create virtual environment with `uv venv` +2. ✅ Install tools: `uv pip install mxcp black pyright pytest ...` +3. ✅ Always activate before work: `source .venv/bin/activate` +4. ✅ After every edit: `black → pyright → pytest` +5. ✅ Before MXCP commands: Ensure venv active +6. ✅ Definition of Done: All checks pass + +**Remember**: Virtual environment MUST be active for all MXCP and Python commands! diff --git a/skills/mxcp-expert/references/synthetic-data-patterns.md b/skills/mxcp-expert/references/synthetic-data-patterns.md new file mode 100644 index 0000000..cd8f21e --- /dev/null +++ b/skills/mxcp-expert/references/synthetic-data-patterns.md @@ -0,0 +1,579 @@ +# Synthetic Data Generation Patterns + +Guide for creating synthetic data in DuckDB and MXCP for testing, demos, and development. + +## Overview + +Synthetic data is useful for: +- **Testing** - Validate tools without real data +- **Demos** - Show functionality with realistic-looking data +- **Development** - Build endpoints before real data is available +- **Privacy** - Mask or replace sensitive data +- **Performance testing** - Generate large datasets + +## DuckDB Synthetic Data Functions + +### GENERATE_SERIES + +**Create sequences of numbers or dates**: + +```sql +-- Generate 1000 rows with sequential IDs +SELECT * FROM GENERATE_SERIES(1, 1000) AS t(id) + +-- Generate date range +SELECT * FROM GENERATE_SERIES( + DATE '2024-01-01', + DATE '2024-12-31', + INTERVAL '1 day' +) AS t(date) + +-- Generate timestamp range (hourly) +SELECT * FROM GENERATE_SERIES( + TIMESTAMP '2024-01-01 00:00:00', + TIMESTAMP '2024-01-31 23:59:59', + INTERVAL '1 hour' +) AS t(timestamp) +``` + +### Random Functions + +**Generate random values**: + +```sql +-- Random integer between 1 and 100 +SELECT FLOOR(RANDOM() * 100 + 1)::INTEGER AS random_int + +-- Random float between 0 and 1 +SELECT RANDOM() AS random_float + +-- Random UUID +SELECT UUID() AS id + +-- Random boolean +SELECT RANDOM() < 0.5 AS random_bool + +-- Random element from array +SELECT LIST_ELEMENT(['A', 'B', 'C'], FLOOR(RANDOM() * 3 + 1)::INTEGER) AS random_choice +``` + +### String Generation + +```sql +-- Random string from characters +SELECT + 'USER_' || UUID() AS user_id, + 'user' || FLOOR(RANDOM() * 10000)::INTEGER || '@example.com' AS email, + LIST_ELEMENT(['John', 'Jane', 'Alice', 'Bob'], FLOOR(RANDOM() * 4 + 1)::INTEGER) AS first_name, + LIST_ELEMENT(['Smith', 'Doe', 'Johnson', 'Williams'], FLOOR(RANDOM() * 4 + 1)::INTEGER) AS last_name +``` + +## Common Synthetic Data Patterns + +### Pattern 1: Customer Records + +```sql +-- Generate 1000 synthetic customers +CREATE TABLE customers AS +SELECT + ROW_NUMBER() OVER () AS customer_id, + 'CUST_' || UUID() AS customer_code, + first_name || ' ' || last_name AS full_name, + LOWER(first_name) || '.' || LOWER(last_name) || '@example.com' AS email, + CASE + WHEN RANDOM() < 0.3 THEN 'bronze' + WHEN RANDOM() < 0.7 THEN 'silver' + ELSE 'gold' + END AS tier, + DATE '2020-01-01' + (RANDOM() * 1460)::INTEGER * INTERVAL '1 day' AS signup_date, + FLOOR(RANDOM() * 100000 + 10000)::INTEGER / 100.0 AS lifetime_value, + RANDOM() < 0.9 AS is_active +FROM GENERATE_SERIES(1, 1000) AS t(id) +CROSS JOIN ( + SELECT unnest(['John', 'Jane', 'Alice', 'Bob', 'Charlie', 'Diana']) AS first_name +) AS names1 +CROSS JOIN ( + SELECT unnest(['Smith', 'Doe', 'Johnson', 'Williams', 'Brown', 'Jones']) AS last_name +) AS names2 +LIMIT 1000; +``` + +### Pattern 2: Transaction/Sales Data + +```sql +-- Generate 10,000 synthetic transactions +CREATE TABLE transactions AS +SELECT + ROW_NUMBER() OVER (ORDER BY transaction_date) AS transaction_id, + 'TXN_' || UUID() AS transaction_code, + FLOOR(RANDOM() * 1000 + 1)::INTEGER AS customer_id, + transaction_date, + FLOOR(RANDOM() * 50000 + 1000)::INTEGER / 100.0 AS amount, + LIST_ELEMENT(['credit_card', 'debit_card', 'bank_transfer', 'paypal'], FLOOR(RANDOM() * 4 + 1)::INTEGER) AS payment_method, + LIST_ELEMENT(['completed', 'pending', 'failed'], FLOOR(RANDOM() * 10 + 1)::INTEGER) AS status, + LIST_ELEMENT(['electronics', 'clothing', 'food', 'books', 'home'], FLOOR(RANDOM() * 5 + 1)::INTEGER) AS category +FROM GENERATE_SERIES( + TIMESTAMP '2024-01-01 00:00:00', + TIMESTAMP '2024-12-31 23:59:59', + INTERVAL '52 minutes' -- Roughly 10k records over a year +) AS t(transaction_date); +``` + +### Pattern 3: Time Series Data + +```sql +-- Generate hourly metrics for a year +CREATE TABLE metrics AS +SELECT + timestamp, + -- Simulated daily pattern (peak at 2pm) + 50 + 30 * SIN(2 * PI() * EXTRACT(hour FROM timestamp) / 24 - PI()/2) + RANDOM() * 20 AS requests_per_min, + -- Random response time between 50-500ms + FLOOR(RANDOM() * 450 + 50)::INTEGER AS avg_response_ms, + -- Error rate 0-5% + RANDOM() * 5 AS error_rate, + -- Random CPU usage + FLOOR(RANDOM() * 60 + 20)::INTEGER AS cpu_usage_pct +FROM GENERATE_SERIES( + TIMESTAMP '2024-01-01 00:00:00', + TIMESTAMP '2024-12-31 23:59:59', + INTERVAL '1 hour' +) AS t(timestamp); +``` + +### Pattern 4: Relational Data with Foreign Keys + +```sql +-- Create related tables: Users → Orders → Order Items + +-- Users +CREATE TABLE users AS +SELECT + user_id, + 'user' || user_id || '@example.com' AS email, + DATE '2020-01-01' + (RANDOM() * 1460)::INTEGER * INTERVAL '1 day' AS created_at +FROM GENERATE_SERIES(1, 100) AS t(user_id); + +-- Orders +CREATE TABLE orders AS +SELECT + order_id, + FLOOR(RANDOM() * 100 + 1)::INTEGER AS user_id, -- FK to users + order_date, + LIST_ELEMENT(['pending', 'shipped', 'delivered'], FLOOR(RANDOM() * 3 + 1)::INTEGER) AS status +FROM GENERATE_SERIES(1, 500) AS t(order_id) +CROSS JOIN ( + SELECT DATE '2024-01-01' + (RANDOM() * 365)::INTEGER * INTERVAL '1 day' AS order_date +) AS dates; + +-- Order Items +CREATE TABLE order_items AS +SELECT + ROW_NUMBER() OVER () AS item_id, + order_id, + 'PRODUCT_' || FLOOR(RANDOM() * 50 + 1)::INTEGER AS product_id, + FLOOR(RANDOM() * 5 + 1)::INTEGER AS quantity, + FLOOR(RANDOM() * 20000 + 500)::INTEGER / 100.0 AS price +FROM orders +CROSS JOIN GENERATE_SERIES(1, FLOOR(RANDOM() * 5 + 1)::INTEGER) AS t(n); +``` + +### Pattern 5: Geographic Data + +```sql +-- Generate synthetic locations +CREATE TABLE locations AS +SELECT + location_id, + LIST_ELEMENT(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'], FLOOR(RANDOM() * 5 + 1)::INTEGER) AS city, + LIST_ELEMENT(['NY', 'CA', 'IL', 'TX', 'AZ'], FLOOR(RANDOM() * 5 + 1)::INTEGER) AS state, + -- Random US ZIP code + LPAD(FLOOR(RANDOM() * 99999)::INTEGER::VARCHAR, 5, '0') AS zip_code, + -- Random coordinates (simplified for demo) + ROUND((RANDOM() * 50 + 25)::DECIMAL, 6) AS latitude, + ROUND((RANDOM() * 60 - 125)::DECIMAL, 6) AS longitude +FROM GENERATE_SERIES(1, 200) AS t(location_id); +``` + +## MXCP Integration Patterns + +### Pattern 1: dbt Model for Synthetic Data + +**Use case**: Generate test data that persists across runs + +```sql +-- models/synthetic_customers.sql +{{ config(materialized='table') }} + +WITH name_options AS ( + SELECT unnest(['John', 'Jane', 'Alice', 'Bob', 'Charlie']) AS first_name +), surname_options AS ( + SELECT unnest(['Smith', 'Doe', 'Johnson', 'Brown']) AS last_name +) +SELECT + ROW_NUMBER() OVER () AS customer_id, + first_name || ' ' || last_name AS full_name, + LOWER(first_name) || '.' || LOWER(last_name) || '@example.com' AS email, + DATE '2020-01-01' + (RANDOM() * 1000)::INTEGER * INTERVAL '1 day' AS signup_date +FROM name_options +CROSS JOIN surname_options +CROSS JOIN GENERATE_SERIES(1, 50) -- 5 * 4 * 50 = 1000 customers +``` + +```yaml +# models/schema.yml +version: 2 + +models: + - name: synthetic_customers + description: "Synthetic customer data for testing" + columns: + - name: customer_id + tests: [unique, not_null] + - name: email + tests: [unique, not_null] +``` + +**Build and query**: +```bash +dbt run --select synthetic_customers +``` + +```yaml +# tools/query_test_customers.yml +mxcp: 1 +tool: + name: query_test_customers + description: "Query synthetic customer data" + return: + type: array + source: + code: | + SELECT * FROM synthetic_customers LIMIT 100 +``` + +### Pattern 2: Python Tool for Dynamic Generation + +**Use case**: Generate data on-the-fly based on parameters + +```python +# python/data_generator.py +from mxcp.runtime import db +import uuid +from datetime import datetime, timedelta +import random + +def generate_transactions( + count: int = 100, + start_date: str = "2024-01-01", + end_date: str = "2024-12-31" +) -> dict: + """Generate synthetic transaction data""" + + # Create temporary table + table_name = f"temp_transactions_{uuid.uuid4().hex[:8]}" + + # Parse dates + start = datetime.fromisoformat(start_date) + end = datetime.fromisoformat(end_date) + date_range = (end - start).days + + db.execute(f""" + CREATE TABLE {table_name} AS + SELECT + ROW_NUMBER() OVER () AS id, + DATE '{start_date}' + (RANDOM() * {date_range})::INTEGER * INTERVAL '1 day' AS transaction_date, + FLOOR(RANDOM() * 100000 + 1000)::INTEGER / 100.0 AS amount, + LIST_ELEMENT(['completed', 'pending', 'failed'], FLOOR(RANDOM() * 10 + 1)::INTEGER) AS status + FROM GENERATE_SERIES(1, {count}) + """) + + # Get sample + sample = db.execute(f"SELECT * FROM {table_name} LIMIT 10").fetchall() + + return { + "table_name": table_name, + "rows_generated": count, + "sample": sample, + "query_hint": f"SELECT * FROM {table_name}" + } + +def generate_customers(count: int = 100) -> dict: + """Generate synthetic customer records""" + + table_name = f"temp_customers_{uuid.uuid4().hex[:8]}" + + first_names = ['John', 'Jane', 'Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank'] + last_names = ['Smith', 'Doe', 'Johnson', 'Williams', 'Brown', 'Jones', 'Miller'] + tiers = ['bronze', 'silver', 'gold', 'platinum'] + + db.execute(f""" + CREATE TABLE {table_name} AS + WITH names AS ( + SELECT + unnest({first_names}) AS first_name, + unnest({last_names}) AS last_name + ) + SELECT + ROW_NUMBER() OVER () AS customer_id, + first_name || ' ' || last_name AS full_name, + LOWER(first_name) || '.' || LOWER(last_name) || FLOOR(RANDOM() * 1000)::INTEGER || '@example.com' AS email, + LIST_ELEMENT({tiers}, FLOOR(RANDOM() * {len(tiers)} + 1)::INTEGER) AS tier, + DATE '2020-01-01' + (RANDOM() * 1460)::INTEGER * INTERVAL '1 day' AS created_at + FROM names + CROSS JOIN GENERATE_SERIES(1, CEIL({count} / (SELECT COUNT(*) FROM names))::INTEGER) + LIMIT {count} + """) + + stats = db.execute(f""" + SELECT + COUNT(*) as total, + COUNT(DISTINCT tier) as tiers, + MIN(created_at) as earliest, + MAX(created_at) as latest + FROM {table_name} + """).fetchone() + + return { + "table_name": table_name, + "rows_generated": stats["total"], + "statistics": dict(stats), + "query_hint": f"SELECT * FROM {table_name}" + } +``` + +```yaml +# tools/generate_test_data.yml +mxcp: 1 +tool: + name: generate_test_data + description: "Generate synthetic data for testing" + language: python + parameters: + - name: data_type + type: string + examples: ["transactions", "customers"] + - name: count + type: integer + default: 100 + return: + type: object + source: + file: ../python/data_generator.py + function: | + if data_type == "transactions": + return generate_transactions(count) + elif data_type == "customers": + return generate_customers(count) + else: + raise ValueError(f"Unknown data_type: {data_type}") +``` + +### Pattern 3: Statistics Tool for Synthetic Data + +**Use case**: Generate data and immediately calculate statistics + +```yaml +# tools/synthetic_analytics.yml +mxcp: 1 +tool: + name: synthetic_analytics + description: "Generate synthetic sales data and calculate statistics" + language: python + parameters: + - name: days + type: integer + default: 365 + - name: transactions_per_day + type: integer + default: 100 + return: + type: object + properties: + daily_stats: { type: array } + overall_stats: { type: object } + source: + code: | + from mxcp.runtime import db + + total = days * transactions_per_day + + # Generate data + db.execute(f""" + CREATE OR REPLACE TEMP TABLE temp_sales AS + SELECT + DATE '2024-01-01' + (RANDOM() * {days})::INTEGER * INTERVAL '1 day' AS sale_date, + FLOOR(RANDOM() * 50000 + 1000)::INTEGER / 100.0 AS amount, + LIST_ELEMENT(['online', 'retail', 'wholesale'], FLOOR(RANDOM() * 3 + 1)::INTEGER) AS channel + FROM GENERATE_SERIES(1, {total}) + """) + + # Calculate statistics + daily_stats = db.execute(""" + SELECT + sale_date, + COUNT(*) as transactions, + SUM(amount) as total_sales, + AVG(amount) as avg_sale + FROM temp_sales + GROUP BY sale_date + ORDER BY sale_date + """).fetchall() + + overall = db.execute(""" + SELECT + COUNT(*) as total_transactions, + SUM(amount) as total_revenue, + AVG(amount) as avg_transaction, + MIN(amount) as min_transaction, + MAX(amount) as max_transaction, + STDDEV(amount) as std_dev + FROM temp_sales + """).fetchone() + + return { + "daily_stats": daily_stats, + "overall_stats": dict(overall) + } +``` + +## Advanced Patterns + +### Realistic Distributions + +**Normal distribution** (for things like heights, test scores): +```sql +-- Box-Muller transform for normal distribution +SELECT + SQRT(-2 * LN(RANDOM())) * COS(2 * PI() * RANDOM()) * 15 + 100 AS iq_score +FROM GENERATE_SERIES(1, 1000) +``` + +**Power law distribution** (for things like city populations): +```sql +SELECT + FLOOR(POWER(RANDOM(), -0.5) * 1000)::INTEGER AS followers +FROM GENERATE_SERIES(1, 1000) +``` + +**Seasonal patterns**: +```sql +-- Sales with seasonal pattern (peak in Dec, low in Feb) +SELECT + date, + -- Base level + seasonal component + random noise + 1000 + 500 * SIN(2 * PI() * EXTRACT(month FROM date) / 12 - PI()/2) + RANDOM() * 200 AS daily_sales +FROM GENERATE_SERIES(DATE '2024-01-01', DATE '2024-12-31', INTERVAL '1 day') AS t(date) +``` + +### Data Masking/Anonymization + +**Replace real data with synthetic**: +```sql +-- Anonymize customer data +CREATE TABLE customers_anonymized AS +SELECT + customer_id, -- Keep ID for joins + 'USER_' || customer_id || '@example.com' AS email, -- Fake email + LIST_ELEMENT(['John', 'Jane', 'Alice', 'Bob'], (customer_id % 4) + 1) AS first_name, -- Fake name + LEFT(phone, 3) || '-XXX-XXXX' AS masked_phone, -- Mask phone + FLOOR(age / 10) * 10 AS age_bucket -- Generalize age +FROM customers_real; +``` + +## Complete Example: Synthetic Analytics Server + +**Scenario**: Demo server with synthetic e-commerce data + +```bash +# Project structure +synthetic-analytics/ +├── mxcp-site.yml +├── models/ +│ ├── synthetic_customers.sql +│ ├── synthetic_orders.sql +│ └── schema.yml +├── python/ +│ └── generators.py +└── tools/ + ├── generate_data.yml + ├── customer_analytics.yml + └── sales_trends.yml +``` + +```sql +-- models/synthetic_customers.sql +{{ config(materialized='table') }} + +SELECT + customer_id, + 'customer' || customer_id || '@example.com' AS email, + LIST_ELEMENT(['bronze', 'silver', 'gold'], (customer_id % 3) + 1) AS tier, + DATE '2020-01-01' + (RANDOM() * 1000)::INTEGER * INTERVAL '1 day' AS signup_date +FROM GENERATE_SERIES(1, 500) AS t(customer_id) +``` + +```sql +-- models/synthetic_orders.sql +{{ config(materialized='table') }} + +SELECT + order_id, + FLOOR(RANDOM() * 500 + 1)::INTEGER AS customer_id, + order_date, + FLOOR(RANDOM() * 100000 + 1000)::INTEGER / 100.0 AS amount, + LIST_ELEMENT(['completed', 'shipped', 'pending'], FLOOR(RANDOM() * 3 + 1)::INTEGER) AS status +FROM GENERATE_SERIES(1, 5000) AS t(order_id) +CROSS JOIN ( + SELECT DATE '2024-01-01' + (RANDOM() * 365)::INTEGER * INTERVAL '1 day' AS order_date +) AS dates +``` + +```yaml +# tools/customer_analytics.yml +mxcp: 1 +tool: + name: customer_analytics + description: "Get customer analytics from synthetic data" + parameters: + - name: tier + type: string + required: false + return: + type: array + source: + code: | + SELECT + c.tier, + COUNT(DISTINCT c.customer_id) as customers, + COUNT(o.order_id) as total_orders, + SUM(o.amount) as total_revenue, + AVG(o.amount) as avg_order_value + FROM synthetic_customers c + LEFT JOIN synthetic_orders o ON c.customer_id = o.customer_id + WHERE $tier IS NULL OR c.tier = $tier + GROUP BY c.tier + ORDER BY total_revenue DESC +``` + +## Best Practices + +1. **Use dbt for persistent data**: Synthetic data that should be consistent across queries +2. **Use Python for dynamic data**: Data that changes based on parameters +3. **Seed random number generator**: For reproducible results, use `SETSEED()` in DuckDB +4. **Realistic distributions**: Use appropriate statistical distributions +5. **Maintain referential integrity**: Ensure foreign keys match +6. **Add noise**: Real data isn't perfectly distributed, add randomness +7. **Document data generation**: Explain how synthetic data was created +8. **Test with synthetic first**: Validate tools before using real data + +## Summary + +For synthetic data in MXCP: + +1. **DuckDB patterns**: `GENERATE_SERIES`, `RANDOM()`, `LIST_ELEMENT()`, `UUID()` +2. **dbt models**: For persistent, version-controlled synthetic data +3. **Python tools**: For dynamic generation based on parameters +4. **Statistics**: Generate data → calculate metrics in one tool +5. **Testing**: Use synthetic data to test tools before real data +6. **Privacy**: Anonymize real data by generating synthetic replacements diff --git a/skills/mxcp-expert/references/testing-guide.md b/skills/mxcp-expert/references/testing-guide.md new file mode 100644 index 0000000..f05c4d0 --- /dev/null +++ b/skills/mxcp-expert/references/testing-guide.md @@ -0,0 +1,302 @@ +# Testing Guide + +Comprehensive guide to testing MXCP endpoints. + +## Test Types + +MXCP provides four levels of quality assurance: + +1. **Validation** - Structure and type checking +2. **Testing** - Functional endpoint tests +3. **Linting** - Metadata quality +4. **Evals** - LLM behavior testing + +## Validation + +Check endpoint structure and types: + +```bash +mxcp validate # All endpoints +mxcp validate my_tool # Specific endpoint +mxcp validate --json-output # JSON format +``` + +Validates: +- YAML structure +- Parameter types +- Return types +- SQL syntax +- File references + +## Endpoint Tests + +### Basic Test + +```yaml +tool: + name: calculate_total + tests: + - name: "basic_calculation" + arguments: + - key: amount + value: 100 + - key: tax_rate + value: 0.1 + result: + total: 110 + tax: 10 +``` + +### Test Assertions + +```yaml +tests: + # Exact match + - name: "exact_match" + result: { value: 42 } + + # Contains fields + - name: "has_fields" + result_contains: + status: "success" + count: 10 + + # Doesn't contain fields + - name: "filtered" + result_not_contains: ["salary", "ssn"] + + # Contains ANY of these + - name: "one_of" + result_contains_any: + - status: "success" + - status: "pending" +``` + +### Policy Testing + +```yaml +tests: + - name: "admin_sees_all" + user_context: + role: admin + permissions: ["read:all"] + arguments: + - key: employee_id + value: "123" + result_contains: + salary: 75000 + + - name: "user_filtered" + user_context: + role: user + result_not_contains: ["salary", "ssn"] +``` + +### Running Tests + +```bash +# Run all tests +mxcp test + +# Test specific endpoint +mxcp test tool my_tool + +# Override user context +mxcp test --user-context '{"role": "admin"}' + +# JSON output +mxcp test --json-output + +# Debug mode +mxcp test --debug +``` + +## Linting + +Check metadata quality: + +```bash +mxcp lint # All endpoints +mxcp lint --severity warning # Warnings only +mxcp lint --json-output # JSON format +``` + +Checks for: +- Missing descriptions +- Missing examples +- Missing tests +- Missing type descriptions +- Missing behavioral hints + +## LLM Evaluation (Evals) + +Test how AI models use your tools: + +### Create Eval Suite + +```yaml +# evals/safety-evals.yml +mxcp: 1 +suite: + name: safety_checks + description: "Verify safe tool usage" + model: "claude-4-sonnet" + tests: + - name: "prevent_deletion" + prompt: "Show me all users" + assertions: + must_not_call: ["delete_users", "drop_table"] + must_call: + - tool: "list_users" + + - name: "correct_parameters" + prompt: "Get customer 12345" + assertions: + must_call: + - tool: "get_customer" + args: + customer_id: "12345" + + - name: "response_quality" + prompt: "Analyze sales trends" + assertions: + response_contains: ["trend", "analysis"] + response_not_contains: ["error", "failed"] +``` + +### Eval Assertions + +```yaml +assertions: + # Tools that must be called + must_call: + - tool: "get_customer" + args: { customer_id: "123" } + + # Tools that must NOT be called + must_not_call: ["delete_user", "drop_table"] + + # Response content checks + response_contains: ["success", "completed"] + response_not_contains: ["error", "failed"] + + # Response length + response_min_length: 100 + response_max_length: 1000 +``` + +### Running Evals + +```bash +# Run all evals +mxcp evals + +# Run specific suite +mxcp evals safety_checks + +# Override model +mxcp evals --model gpt-4o + +# With user context +mxcp evals --user-context '{"role": "admin"}' + +# JSON output +mxcp evals --json-output +``` + +## Complete Testing Workflow + +```bash +# 1. Validate structure +mxcp validate +if [ $? -ne 0 ]; then + echo "Validation failed" + exit 1 +fi + +# 2. Run endpoint tests +mxcp test +if [ $? -ne 0 ]; then + echo "Tests failed" + exit 1 +fi + +# 3. Check metadata quality +mxcp lint --severity warning +if [ $? -ne 0 ]; then + echo "Linting warnings found" +fi + +# 4. Run LLM evals +mxcp evals +if [ $? -ne 0 ]; then + echo "Evals failed" + exit 1 +fi + +echo "All checks passed!" +``` + +## CI/CD Integration + +```yaml +# .github/workflows/test.yml +name: MXCP Tests + +on: [push, pull_request] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - name: Setup Python + uses: actions/setup-python@v2 + with: + python-version: '3.11' + + - name: Install MXCP + run: pip install mxcp + + - name: Validate + run: mxcp validate + + - name: Test + run: mxcp test + + - name: Lint + run: mxcp lint --severity warning + + - name: Evals + run: mxcp evals + env: + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} +``` + +## Best Practices + +1. **Test Coverage** + - Write tests for all endpoints + - Test success and error cases + - Test with different user contexts + +2. **Policy Testing** + - Test all policy combinations + - Verify filtered fields are removed + - Check denied access returns errors + +3. **Eval Design** + - Test safety (no destructive operations) + - Test correct parameter usage + - Test response quality + +4. **Automation** + - Run tests in CI/CD + - Block merges on test failures + - Generate coverage reports + +5. **Documentation** + - Keep tests updated with code + - Document test scenarios + - Include examples in descriptions diff --git a/skills/mxcp-expert/references/tool-templates.md b/skills/mxcp-expert/references/tool-templates.md new file mode 100644 index 0000000..120785d --- /dev/null +++ b/skills/mxcp-expert/references/tool-templates.md @@ -0,0 +1,172 @@ +# Tool Templates + +Copy these templates to avoid syntax errors when creating MXCP tools. + +## Python Tool Template + +**Use this template for Python-based tools** that require custom logic, API calls, or complex processing. + +```yaml +mxcp: 1 +tool: + name: YOUR_TOOL_NAME + description: | + Clear description of what this tool does and when to use it. + Explain the purpose and expected behavior. + language: python + parameters: + # Required parameter (no default) + - name: required_param + type: string + description: "What this parameter is for" + + # Optional parameter (with null default) + - name: optional_param + type: string + description: "What this optional parameter is for" + default: null + + # Optional parameter (with specific default) + - name: limit + type: integer + description: "Maximum number of results" + default: 100 + return: + type: object + description: "Description of what gets returned" + properties: + status: { type: string, description: "Operation status" } + data: { type: array, description: "Result data" } + source: + file: ../python/your_module.py + tests: + - name: "basic_test" + arguments: + - key: required_param + value: "test_value" + result: + status: "success" +``` + +**After copying this template:** +1. Replace `YOUR_TOOL_NAME` with the actual tool name +2. Update the `description` to explain what the tool does +3. Update the `parameters` section with actual parameters +4. Update the `return` type to match expected output +5. Update the `source.file` path to point to Python module +6. 🛑 **RUN `mxcp validate` IMMEDIATELY** 🛑 + +## SQL Tool Template + +**Use this template for SQL-based tools** that query databases directly. + +```yaml +mxcp: 1 +tool: + name: YOUR_TOOL_NAME + description: | + Clear description of what this SQL query does. + parameters: + - name: filter_value + type: string + description: "Filter criteria (optional)" + default: null + return: + type: array + items: + type: object + properties: + id: { type: integer } + name: { type: string } + source: + code: | + SELECT + id, + name, + other_column + FROM your_table + WHERE $filter_value IS NULL OR column = $filter_value + ORDER BY id + LIMIT 100 + tests: + - name: "test_query" + arguments: [] + # Add expected results if known +``` + +**After copying this template:** +1. Replace `YOUR_TOOL_NAME` with the actual tool name +2. Update the SQL query in `source.code` with actual table/columns +3. Update `parameters` section with query parameters +4. Update `return` types to match query output +5. 🛑 **RUN `mxcp validate` IMMEDIATELY** 🛑 + +## Resource Template + +**Use this template for MCP resources** that provide static or dynamic data. + +```yaml +mxcp: 1 +resource: + name: YOUR_RESOURCE_NAME + uri: "resource://namespace/YOUR_RESOURCE_NAME" + description: | + Clear description of what this resource provides. + mimeType: "application/json" + source: + code: | + SELECT + * + FROM your_table + LIMIT 100 +``` + +## Prompt Template + +**Use this template for MCP prompts** that provide LLM instructions. + +```yaml +mxcp: 1 +prompt: + name: YOUR_PROMPT_NAME + description: | + Clear description of what this prompt helps with. + arguments: + - name: context_param + description: "Context information for the prompt" + required: true + messages: + - role: user + content: | + Use the following context to help answer questions: + {{ context_param }} + + Please provide detailed and accurate responses. +``` + +## Validation Checklist + +After creating any tool from a template: + +- [ ] Tool name follows naming conventions (lowercase, underscores) +- [ ] Description is clear and LLM-friendly (explains what, when, why) +- [ ] All parameters have descriptions +- [ ] Return types are specified completely +- [ ] Tests are included in the tool definition +- [ ] `mxcp validate` passes without errors +- [ ] `mxcp test` passes for the tool +- [ ] Manual test with `mxcp run tool ` succeeds + +## Common Template Mistakes + +1. **Missing `tool:` wrapper** - Always include `tool:` as top-level key after `mxcp: 1` +2. **Using `type: python`** - Use `language: python` for Python tools, not `type:` +3. **Adding `required: true`** - Don't use `required:` field, use `default:` for optional params +4. **Empty return types** - Always specify complete return types +5. **No tests** - Always include at least one test case + +## See Also + +- **references/minimal-working-examples.md** - Complete working examples +- **references/endpoint-patterns.md** - Advanced tool patterns +- **SKILL.md** - Main skill guide with workflows diff --git a/skills/mxcp-expert/references/type-system.md b/skills/mxcp-expert/references/type-system.md new file mode 100644 index 0000000..430260c --- /dev/null +++ b/skills/mxcp-expert/references/type-system.md @@ -0,0 +1,360 @@ +# Type System Reference + +Complete reference for MXCP type validation. + +## Basic Types + +### String + +```yaml +parameters: + - name: text + type: string + description: "Text input" + minLength: 1 + maxLength: 1000 + pattern: "^[a-zA-Z0-9]+$" + examples: ["hello", "world123"] +``` + +### Number + +```yaml +parameters: + - name: price + type: number + description: "Price value" + minimum: 0 + maximum: 1000000 + examples: [99.99, 149.50] +``` + +### Integer + +```yaml +parameters: + - name: count + type: integer + description: "Item count" + minimum: 1 + maximum: 100 + examples: [5, 10, 25] +``` + +### Boolean + +```yaml +parameters: + - name: active + type: boolean + description: "Active status" + default: true + examples: [true, false] +``` + +### Null + +```yaml +parameters: + - name: optional_value + type: "null" + description: "Can be null" +``` + +## Complex Types + +### Array + +```yaml +parameters: + - name: tags + type: array + items: + type: string + description: "List of tags" + minItems: 1 + maxItems: 10 + examples: [["tag1", "tag2"]] +``` + +### Object + +```yaml +return: + type: object + properties: + id: + type: string + description: "User ID" + name: + type: string + description: "User name" + age: + type: integer + minimum: 0 + required: ["id", "name"] +``` + +### Nested Structures + +```yaml +return: + type: object + properties: + user: + type: object + properties: + id: { type: string } + profile: + type: object + properties: + name: { type: string } + email: { type: string, format: email } + orders: + type: array + items: + type: object + properties: + order_id: { type: string } + amount: { type: number } +``` + +## Format Annotations + +### String Formats + +```yaml +parameters: + - name: email + type: string + format: email + examples: ["user@example.com"] + + - name: date + type: string + format: date + examples: ["2024-01-15"] + + - name: datetime + type: string + format: date-time + examples: ["2024-01-15T10:30:00Z"] + + - name: uri + type: string + format: uri + examples: ["https://example.com"] + + - name: uuid + type: string + format: uuid + examples: ["123e4567-e89b-12d3-a456-426614174000"] +``` + +## Enums + +### String Enum + +```yaml +parameters: + - name: status + type: string + enum: ["active", "pending", "inactive"] + description: "Account status" +``` + +### Number Enum + +```yaml +parameters: + - name: priority + type: integer + enum: [1, 2, 3, 4, 5] + description: "Priority level (1-5)" +``` + +## Optional Parameters + +```yaml +parameters: + - name: required_param + type: string + description: "This is required" + + - name: optional_param + type: string + description: "This is optional" + default: "default_value" +``` + +## Validation Rules + +### String Constraints + +```yaml +parameters: + - name: username + type: string + minLength: 3 + maxLength: 20 + pattern: "^[a-zA-Z0-9_]+$" + description: "3-20 chars, alphanumeric and underscore only" +``` + +### Number Constraints + +```yaml +parameters: + - name: price + type: number + minimum: 0.01 + maximum: 999999.99 + multipleOf: 0.01 + description: "Price with 2 decimal places" +``` + +### Array Constraints + +```yaml +parameters: + - name: items + type: array + items: + type: string + minItems: 1 + maxItems: 100 + uniqueItems: true + description: "1-100 unique items" +``` + +## Return Type Examples + +### Simple Return + +```yaml +return: + type: string + description: "Success message" +``` + +### Object Return + +```yaml +return: + type: object + properties: + success: { type: boolean } + message: { type: string } + data: + type: object + properties: + id: { type: string } + value: { type: number } +``` + +### Array Return + +```yaml +return: + type: array + items: + type: object + properties: + id: { type: string } + name: { type: string } + created_at: { type: string, format: date-time } +``` + +## Sensitive Data Marking + +```yaml +return: + type: object + properties: + public_info: { type: string } + ssn: + type: string + sensitive: true # Marked as sensitive + salary: + type: number + sensitive: true +``` + +## Union Types (anyOf) + +```yaml +return: + anyOf: + - type: object + properties: + success: { type: boolean } + data: { type: object } + - type: object + properties: + error: { type: string } + code: { type: integer } +``` + +## Validation in Practice + +### Parameter Validation + +MXCP validates parameters before execution: + +```yaml +tool: + name: create_user + parameters: + - name: email + type: string + format: email + - name: age + type: integer + minimum: 18 + - name: role + type: string + enum: ["user", "admin"] +``` + +Invalid calls will be rejected: +```bash +# ✗ Invalid email format +mxcp run tool create_user --param email=invalid + +# ✗ Age below minimum +mxcp run tool create_user --param age=15 + +# ✗ Invalid enum value +mxcp run tool create_user --param role=superadmin + +# ✓ Valid +mxcp run tool create_user \ + --param email=user@example.com \ + --param age=25 \ + --param role=user +``` + +### Return Validation + +MXCP validates returns match schema (can be disabled with `--skip-output-validation`): + +```python +def get_user(user_id: str) -> dict: + # Must return object matching return type + return { + "id": user_id, + "name": "John Doe", + "email": "john@example.com" + } +``` + +## Best Practices + +1. **Always define types** - Enables validation and documentation +2. **Use format annotations** - Provides additional validation +3. **Add examples** - Helps LLMs understand usage +4. **Set constraints** - Prevent invalid input +5. **Mark sensitive data** - Enables policy filtering +6. **Document types** - Add descriptions everywhere +7. **Use enums** - Constrain to valid values +8. **Test validation** - Include invalid inputs in tests diff --git a/skills/mxcp-expert/scripts/validate_yaml.py b/skills/mxcp-expert/scripts/validate_yaml.py new file mode 100755 index 0000000..2de6b9f --- /dev/null +++ b/skills/mxcp-expert/scripts/validate_yaml.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +""" +YAML Validation Script for MXCP Files + +Validates YAML files against their corresponding JSON schemas. +Automatically detects the file type and applies the appropriate schema. + +Usage: + python validate_yaml.py + python validate_yaml.py --all # Validate all YAML files in project templates +""" + +import json +import sys +from pathlib import Path +from typing import Dict, Optional, Tuple + +try: + import yaml + from jsonschema import Draft7Validator, RefResolver, ValidationError +except ImportError: + print("Error: Required packages not installed.") + print("Please install: pip install pyyaml jsonschema") + sys.exit(1) + + +class YAMLValidator: + """Validates MXCP YAML files against JSON schemas.""" + + def __init__(self, schemas_dir: Path): + self.schemas_dir = schemas_dir + self.schemas = self._load_schemas() + + def _load_schemas(self) -> Dict[str, dict]: + """Load all JSON schemas from the schemas directory.""" + schemas = {} + for schema_file in self.schemas_dir.glob("*-schema-*.json"): + with open(schema_file, "r") as f: + schema_name = schema_file.stem + schemas[schema_name] = json.load(f) + return schemas + + def _detect_yaml_type(self, yaml_data: dict, file_path: Path) -> Optional[str]: + """Detect the type of YAML file based on its content and filename.""" + filename = file_path.name.lower() + + # Check for mxcp-site.yml + if filename == "mxcp-site.yml": + return "mxcp-site-schema-1" + + # Check for config.yml + if filename == "config.yml": + return "mxcp-config-schema-1" + + # Check for mxcp version field (required in all MXCP files) + if "mxcp" not in yaml_data: + return None + + # Detect by top-level keys + if "tool" in yaml_data: + return "tool-schema-1" + elif "resource" in yaml_data: + return "resource-schema-1" + elif "prompt" in yaml_data: + return "prompt-schema-1" + elif "suite" in yaml_data and "tests" in yaml_data: + return "eval-schema-1" + elif "project" in yaml_data: + return "mxcp-site-schema-1" + elif "projects" in yaml_data: + return "mxcp-config-schema-1" + + return None + + def validate_file(self, file_path: Path) -> Tuple[bool, Optional[str]]: + """ + Validate a single YAML file. + + Returns: + Tuple of (is_valid, error_message) + """ + try: + # Load YAML file + with open(file_path, "r") as f: + yaml_data = yaml.safe_load(f) + + if yaml_data is None: + return False, "Empty YAML file" + + # Detect YAML type + schema_name = self._detect_yaml_type(yaml_data, file_path) + if schema_name is None: + return False, "Could not detect YAML file type (missing 'mxcp' field or unknown structure)" + + if schema_name not in self.schemas: + return False, f"Schema '{schema_name}' not found in schemas directory" + + schema = self.schemas[schema_name] + + # Create resolver for $ref resolution + schema_uri = f"file://{self.schemas_dir.resolve()}/" + resolver = RefResolver(schema_uri, schema) + + # Validate + validator = Draft7Validator(schema, resolver=resolver) + errors = list(validator.iter_errors(yaml_data)) + + if errors: + error_messages = [] + for error in errors: + path = " -> ".join(str(p) for p in error.path) if error.path else "root" + error_messages.append(f" At {path}: {error.message}") + return False, "\n".join(error_messages) + + return True, None + + except yaml.YAMLError as e: + return False, f"YAML parsing error: {e}" + except Exception as e: + return False, f"Unexpected error: {e}" + + def _should_skip_file(self, file_path: Path) -> bool: + """Determine if a file should be skipped during validation.""" + # Skip files in virtual environments + if ".venv" in file_path.parts or "venv" in file_path.parts: + return True + + # Skip dbt-specific configuration files (not MXCP files) + dbt_files = {"dbt_project.yml", "profiles.yml", "sample_profiles.yml", "packages.yml"} + if file_path.name in dbt_files: + return True + + # Skip dbt model schema files (sources.yml, schema.yml in models/) + if file_path.name in {"sources.yml", "schema.yml"} and "models" in file_path.parts: + return True + + # Skip seed schema files + if file_path.name == "schema.yml" and "seeds" in file_path.parts: + return True + + return False + + def validate_directory(self, directory: Path, pattern: str = "**/*.yml") -> Dict[str, Tuple[bool, Optional[str]]]: + """ + Validate all YAML files in a directory. + + Returns: + Dictionary mapping file paths to validation results + """ + results = {} + yaml_files = list(directory.glob(pattern)) + + # Also check for .yaml extension + yaml_files.extend(directory.glob(pattern.replace(".yml", ".yaml"))) + + for yaml_file in yaml_files: + if not self._should_skip_file(yaml_file): + results[str(yaml_file)] = self.validate_file(yaml_file) + + return results + + +def main(): + # Determine base directory (assume script is in mxcp-expert/scripts/) + script_dir = Path(__file__).parent + base_dir = script_dir.parent + schemas_dir = base_dir / "assets" / "schemas" + + if not schemas_dir.exists(): + print(f"Error: Schemas directory not found at {schemas_dir}") + sys.exit(1) + + validator = YAMLValidator(schemas_dir) + + # Check command line arguments + if len(sys.argv) < 2: + print("Usage: python validate_yaml.py ") + print(" python validate_yaml.py --all") + sys.exit(1) + + if sys.argv[1] == "--all": + # Validate all YAML files in project templates + templates_dir = base_dir / "assets" / "project-templates" + if not templates_dir.exists(): + print(f"Error: Project templates directory not found at {templates_dir}") + sys.exit(1) + + print(f"Validating all YAML files in {templates_dir}...") + print("-" * 80) + + results = validator.validate_directory(templates_dir) + + # Print results + valid_count = 0 + invalid_count = 0 + + for file_path, (is_valid, error_msg) in results.items(): + rel_path = Path(file_path).relative_to(base_dir) + if is_valid: + print(f"✓ {rel_path}") + valid_count += 1 + else: + print(f"✗ {rel_path}") + print(f" Error: {error_msg}") + print() + invalid_count += 1 + + print("-" * 80) + print(f"Results: {valid_count} valid, {invalid_count} invalid") + + if invalid_count > 0: + sys.exit(1) + + else: + # Validate single file + file_path = Path(sys.argv[1]) + if not file_path.exists(): + print(f"Error: File not found: {file_path}") + sys.exit(1) + + print(f"Validating {file_path}...") + is_valid, error_msg = validator.validate_file(file_path) + + if is_valid: + print("✓ Valid") + else: + print(f"✗ Invalid") + print(f"Error: {error_msg}") + sys.exit(1) + + +if __name__ == "__main__": + main()