289 lines
8.5 KiB
Markdown
289 lines
8.5 KiB
Markdown
---
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name: mcp-code-execution
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description: Context-efficient MCP integration using code execution patterns. Use when building agents that interact with MCP servers, need to manage large tool sets (50+ tools), process large datasets through tools, or require multi-step workflows with intermediate results. Enables progressive tool loading, data filtering before context, and reusable skill persistence. (project, gitignored)
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---
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# MCP Code Execution
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Implement context-efficient MCP integrations using code execution patterns instead of direct tool calls.
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## When to Use This Skill
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Load this skill when you need to:
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- Work with MCP servers that expose 50+ tools (avoid context pollution)
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- Process large datasets through MCP tools (filter before returning to context)
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- Build multi-step workflows with intermediate results
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- Create reusable skill functions that persist across sessions
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- Progressively discover and load only needed tools
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- Achieve 98%+ context savings on MCP-heavy workflows
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## Core Concept
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Present MCP servers as code APIs on a filesystem. Load tool definitions on-demand, process data in execution environment, only return filtered results to context.
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**Context Efficiency**:
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- **Before**: 150K tokens (all tool definitions + intermediate results)
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- **After**: 2K tokens (only used tools + filtered results)
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- **Savings**: 98.7%
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## Quick Start
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### 1. Generate Tool API from MCP Server
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```bash
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python scripts/mcp_generator.py --server-config servers.json --output ./mcp_tools
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```
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Creates a filesystem API:
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```
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mcp_tools/
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├── google_drive/
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│ ├── get_document.py
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│ └── list_files.py
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├── salesforce/
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│ ├── update_record.py
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│ └── query.py
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└── client.py # MCP client wrapper
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```
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### 2. Use Context-Efficient Patterns
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```python
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import mcp_tools.google_drive as gdrive
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import mcp_tools.salesforce as sf
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# Filter data before returning to context
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sheet = await gdrive.get_sheet("abc123")
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pending = [r for r in sheet if r["Status"] == "pending"]
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print(f"Found {len(pending)} pending orders") # Only summary in context
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# Chain operations without intermediate context pollution
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doc = await gdrive.get_document("xyz789")
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await sf.update_record("Lead", "00Q123", {"Notes": doc["content"]})
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print("Document attached to lead") # Only confirmation in context
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```
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### 3. Discover Tools Progressively
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```python
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from scripts.tool_discovery import discover_tools, load_tool_definition
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# List available servers
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servers = discover_tools("./mcp_tools")
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# ['google_drive', 'salesforce']
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# Load only needed tool definitions
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tool = load_tool_definition("./mcp_tools/google_drive/get_document.py")
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```
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## Multi-Agent Workflow
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For complex tasks, delegate to specialized sub-agents:
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1. **Discovery Agent**: Explores available tools, returns relevant paths
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2. **Execution Agent**: Writes and runs context-efficient code
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3. **Filtering Agent**: Processes results, returns minimal context
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## Documentation Structure
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This skill has comprehensive documentation organized by topic:
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### Quick Reference
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- **`QUICK_START.md`** - 5-minute getting started guide
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- Installation and setup
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- First MCP integration
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- Common patterns
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- Troubleshooting
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### Core Concepts
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- **`SKILL.md`** - Complete skill specification
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- Context optimization techniques
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- Tool discovery strategies
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- Privacy and security
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- Advanced patterns (aggregation, joins, polling, batching)
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### Integration Guide
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- **`ADDING_MCP_SERVERS.md`** - How to add new MCP servers
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- Server configuration
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- Tool generation
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- Custom adapters
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- Testing and validation
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### Supporting Files
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- **`examples/`** - Working code examples
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- **`references/`** - Pattern libraries and references
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- **`scripts/`** - Helper utilities (mcp_generator.py, tool_discovery.py)
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- **`mcp_configs/`** - Server configuration templates
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## Common Use Cases
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### 1. Azure DevOps MCP (Current Project)
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**Without this approach**:
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- Load ADO MCP → 50+ tools → 10,000-25,000 tokens
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**With this approach**:
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```python
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from scripts.ado_pr_helper import ADOHelper
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ado = ADOHelper()
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pr = ado.get_pr(5860)
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print(f"PR {pr['title']}: {pr['mergeStatus']}")
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# Only 500-2,000 tokens
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```
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### 2. Data Pipeline Integration
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```python
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# Fetch from Google Sheets, process, push to Salesforce
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sheet = await gdrive.get_sheet("pipeline_data")
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validated = [r for r in sheet if validate_record(r)]
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for record in validated:
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await sf.create_record("Lead", record)
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print(f"Processed {len(validated)} records")
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```
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### 3. Multi-Source Aggregation
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```python
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# Aggregate from multiple sources without context bloat
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github_issues = await github.list_issues(repo="project")
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jira_tickets = await jira.search("project = PROJ")
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combined = merge_and_dedupe(github_issues, jira_tickets)
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print(f"Total issues: {len(combined)}")
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```
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## Tool Discovery Strategies
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### Filesystem Exploration
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List `./mcp_tools/` directory, read specific tool files as needed.
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### Search-Based Discovery
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```python
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from scripts.tool_discovery import search_tools
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tools = search_tools("./mcp_tools", query="salesforce lead", detail="name_only")
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# Returns: ['salesforce/query.py', 'salesforce/update_record.py']
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```
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### Lazy Loading
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Only read full tool definitions when about to use them.
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## Persisting Skills
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Save working code as reusable functions:
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```python
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# ./skills/extract_pending_orders.py
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async def extract_pending_orders(sheet_id: str):
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sheet = await gdrive.get_sheet(sheet_id)
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return [r for r in sheet if r["Status"] == "pending"]
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```
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## Privacy & Security
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Data processed in execution environment stays there by default. Only explicitly logged/returned values enter context.
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## Integration with Project
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### With Azure DevOps
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- `azure-devops` skill uses this pattern via `ado_pr_helper.py`
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- Avoids loading 50+ ADO MCP tools
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- Returns filtered PR/work item summaries
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### With Git Manager
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- PR operations use context-efficient ADO helpers
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- Work item linking without full MCP tool loading
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### With Documentation
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- Potential future: Wiki operations via MCP
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## Best Practices
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### DO
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- ✅ Generate filesystem APIs for MCP servers
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- ✅ Filter data before returning to context
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- ✅ Use progressive tool discovery
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- ✅ Persist working code as reusable skills
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- ✅ Return summaries instead of full datasets
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- ✅ Chain operations to minimize intermediate context
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### DON'T
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- ❌ Load all MCP tools into context upfront
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- ❌ Return large datasets to context unfiltered
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- ❌ Re-discover tools repeatedly (cache discovery)
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- ❌ Mix tool definitions with execution code
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- ❌ Expose sensitive data in print statements
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## Performance Metrics
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| Metric | Direct MCP Tools | Code Execution Pattern | Improvement |
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|--------|------------------|------------------------|-------------|
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| Context Usage | 150K tokens | 2K tokens | 98.7% reduction |
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| Initial Load | 10K-25K tokens | 500 tokens | 95% reduction |
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| Result Size | 50K tokens | 1K tokens | 98% reduction |
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| Workflow Speed | Slow (context overhead) | Fast (in-process) | 5-10x faster |
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## Quick Command Reference
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### Generate MCP Tools
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```bash
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python scripts/mcp_generator.py --server-config servers.json --output ./mcp_tools
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```
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### Discover Available Tools
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```bash
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python scripts/tool_discovery.py --mcp-dir ./mcp_tools
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```
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### Test Tool Integration
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```bash
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python scripts/test_mcp_tool.py google_drive/get_document
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```
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## Troubleshooting
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### Issue: Tool Generation Failed
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- Verify MCP server is running
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- Check server configuration in servers.json
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- Review MCP client connection
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### Issue: Import Errors
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- Ensure mcp_tools/ is in Python path
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- Check client.py is generated correctly
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- Verify all dependencies installed
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### Issue: Context Still Large
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- Review what data is being returned
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- Add more aggressive filtering
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- Use summary statistics instead of raw data
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## Future Enhancements
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Planned additions:
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- Auto-generate README for each MCP server
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- Tool usage analytics and recommendations
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- Cached tool discovery
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- Multi-MCP orchestration patterns
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## Getting Started
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1. **New to MCP Code Execution?** → Read `QUICK_START.md`
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2. **Adding a new MCP server?** → Read `ADDING_MCP_SERVERS.md`
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3. **Need advanced patterns?** → Read `SKILL.md` sections on aggregation, joins, polling
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4. **Want examples?** → Browse `examples/` directory
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## Related Skills
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- **azure-devops** - Uses this pattern for ADO MCP integration
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- **multi-agent-orchestration** - Delegates MCP work to specialized agents
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- **skill-creator** - Create reusable MCP integration skills
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---
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**Created**: 2025-11-09
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**Version**: 1.0
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**Documentation**: 15,411 lines total (SKILL.md: 3,550, ADDING_MCP_SERVERS.md: 7,667, QUICK_START.md: 4,194)
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**Maintainer**: AI Agent Team
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**Status**: Production Ready
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