PyTorch Documentation Search Tool

# PyTorch Documentation Search MCP: Refactoring Implementation Summary This document summarizes the refactoring implementation performed on the PyTorch Documentation Search MCP integration. ## Refactoring Goals 1. Consolidate duplicate MCP implementations 2. Standardize on MCP schema version 1.0 3. Streamline transport mechanisms 4. Improve code organization and maintainability ## Changes Implemented ### 1. Unified Server Implementation - Created a single server implementation in `ptsearch/server.py` - Eliminated duplicate code between `mcp_server_pytorch/server.py` and `ptsearch/mcp.py` - Implemented support for both STDIO and SSE transports in one codebase - Standardized search handler interface ### 2. Protocol Standardization - Updated tool descriptor in `ptsearch/protocol/descriptor.py` to use schema version 1.0 - Consolidated all tool descriptor references to a single source of truth - Standardized handling of filter enums with empty string as canonical representation ### 3. Transport Layer Improvements - Enhanced transport implementations with better error handling - Simplified the SSE transport implementation while maintaining compatibility - Ensured consistent request/response handling across transports ### 4. Entry Point Standardization - Updated `mcp_server_pytorch/__main__.py` to use the unified server implementation - Maintained backward compatibility for existing entry points - Streamlined the arguments handling for all script entry points ### 5. Script Updates - Updated all shell scripts (`run_mcp.sh`, `run_mcp_uvx.sh`, `register_mcp.sh`) to use the new implementations - Added better error handling and environment variable validation - Ensured consistent paths and configuration across all integration methods ## Benefits of Refactoring 1. **Code Maintainability**: Single implementation reduces duplication and simplifies future changes 2. **Standards Compliance**: Consistent use of MCP schema 1.0 across all components 3. **Error Handling**: Improved logging and error reporting 4. **Deployment Flexibility**: Clear and consistent methods for different deployment scenarios ## Testing and Validation All integration methods were tested: 1. STDIO transport using direct Python execution 2. SSE transport with Flask server 3. Command-line interfaces for both approaches ## Future Improvements 1. Enhanced caching for embedding generation to improve performance 2. Better search ranking algorithms 3. Support for more PyTorch documentation sources ## Conclusion The refactoring provides a cleaner, more maintainable implementation of the PyTorch Documentation Search MCP integration with Claude Code, ensuring consistent behavior across different transport mechanisms and deployment scenarios.