38 lines
1.1 KiB
Markdown
38 lines
1.1 KiB
Markdown
# Phase 1: Discovery & Language Selection
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**Purpose**: Understand what the user wants to build and choose the right SDK
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## Questions to Ask
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### 1. Server Purpose
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- "What data source, tools, or workflows do you want to expose to AI?"
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- Examples: "Access PostgreSQL database", "Search Jira tickets", "Format code"
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### 2. Target AI Application
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- Claude Desktop (most common)
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- Custom AI application
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- Multiple clients
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### 3. Programming Language Preference
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- **TypeScript/Node.js** (recommended for web APIs, JavaScript ecosystem)
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- **Python** (recommended for data processing, ML workflows)
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- **Java/Spring AI** (enterprise Java applications)
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- **Kotlin** (Android/JVM applications)
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- **C#/.NET** (Windows/Azure applications)
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### 4. Capability Types Needed
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- **Tools**: Functions AI can call (e.g., "get_weather", "search_database")
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- **Resources**: Data AI can read (e.g., file contents, API responses)
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- **Prompts**: Specialized templates for common tasks
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## Output
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Clear understanding of:
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- Server purpose
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- Language choice
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- Capabilities needed (tools/resources/prompts)
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## Transition
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Proceed to Phase 2 (Project Structure Generation)
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