PyTorch Documentation Search Tool

# PyTorch Documentation Search - Refactoring Results ## Objectives Achieved The refactoring of the PyTorch Documentation Search tool has been successfully completed with the following key objectives achieved: 1. ✅ **Consolidated MCP Implementations**: Created a single unified server implementation 2. ✅ **Protocol Standardization**: Updated all code to use MCP schema version 1.0 3. ✅ **Transport Streamlining**: Simplified transport mechanisms with better abstractions 4. ✅ **Organization Improvement**: Implemented cleaner code organization with better separation of concerns ## Key Changes ### 1. Server Implementation - ✅ Created unified `ptsearch/server.py` replacing duplicate implementations - ✅ Implemented a single search handler with consistent interface - ✅ Added proper error handling and logging throughout - ✅ Standardized result formatting for both transport types ### 2. Protocol Handling - ✅ Updated `protocol/descriptor.py` to standardize on schema version 1.0 - ✅ Used centralized settings for tool configuration - ✅ Created consistent handling for all protocol messages - ✅ Fixed filter enum handling to use empty string standard ### 3. Transport Mechanisms - ✅ Enhanced STDIO transport with better error handling and lifecycle management - ✅ Improved SSE transport implementation for Flask - ✅ Created consistent interfaces for both transport mechanisms - ✅ Standardized response handling across all transports ### 4. Entry Points & Scripts - ✅ Updated `mcp_server_pytorch/__main__.py` to use the new unified server - ✅ Improved shell scripts for better environment validation - ✅ Added clearer error messages for common setup issues - ✅ Standardized argument handling across all interfaces ## Integration Methods The refactored code supports three integration methods: 1. **STDIO Integration** (Local Development): - Using `run_mcp.sh` and `register_mcp.sh` - Direct communication with Claude CLI 2. **SSE Integration** (Server Deployment): - HTTP/SSE transport over port 5000 - Registration with `claude mcp add search_pytorch_docs http://localhost:5000/events --transport sse` 3. **UVX Integration** (Packaged Distribution): - Using `run_mcp_uvx.sh` - Prepackaged deployments with environment isolation ## Future Work While the core refactoring is complete, some opportunities for future improvement include: 1. Enhanced caching for embedding generation 2. Better search ranking algorithms 3. Support for additional PyTorch documentation sources 4. Improved performance metrics and monitoring 5. Configuration file support for persistent settings ## Conclusion The refactoring provides a cleaner, more maintainable implementation of the PyTorch Documentation Search tool with Claude Code MCP integration. The unified architecture ensures consistent behavior across different transport mechanisms and deployment scenarios, making the tool more reliable and easier to maintain going forward.