Claude Server MCP

# Claude Server MCP: Python Rewrite Strategy ## Overview This document outlines the strategy for rewriting the Claude Server MCP from JavaScript/TypeScript to Python. This fundamental change will allow for improved maintainability, better performance, and integration with Python-based AI tooling. ## Technology Stack ### Core Technologies - **Language**: Python 3.10+ (for stability and modern features) - **Data Validation**: Pydantic (for schema definition and validation) - **API Framework**: FastAPI (for async capabilities and automatic OpenAPI docs) - **Storage**: JSON-based file storage initially with a path to SQLite/PostgreSQL ### Potential Integrations - **Pydantic AI**: Explore AI-enhanced schema generation and validation - **LangChain/LlamaIndex**: For potential context management enhancements - **Session Management**: Custom solution for Claude session tracking ## Feature Migration Map | Current Feature | Migration Priority | Notes | |-----------------|-------------------|-------| | Basic MCP Protocol | High | Core functionality for client communication | | Context Storage | High | Essential but needs redesign for better UX | | Project Context Management | Medium | Useful but needs better UX | | Context Tagging | Medium | Beneficial for organization | | Parent-Child Relationships | Low | Advanced feature, can come later | | Reference Linking | Low | Advanced feature, can come later | | Cross-session Context | High | Critical UX improvement needed | ## UX Improvements ### Session Management Challenges Current implementation requires: 1. A Claude session ID for context association 2. Manual tracking of project IDs 3. Explicit context loading in new sessions ### Proposed Solutions 1. **Automatic Session Association** - Use conversation fingerprinting to associate contexts - Create persistent client identifiers - Build intelligence to suggest relevant contexts 2. **Context Discovery** - Implement search by content/keywords - Create context browsing capabilities - Add smart context recommendations 3. **Simplified API** - Reduce required parameters - Add sensible defaults - Implement progressive disclosure pattern ## Implementation Approach ### Phase 1: Core Infrastructure (2-4 weeks) 1. **Setup Python Project Structure** - Define module organization - Setup dependency management - Configure development environment 2. **Implement MCP Protocol Basics** - Create MCP server in Python - Implement basic tools interface - Ensure protocol compatibility 3. **Create Storage Layer** - Design improved storage schema - Implement file-based storage - Create migration tool for existing data ### Phase 2: Feature Implementation (4-6 weeks) 1. **Context Management Core** - Implement context CRUD operations - Add tagging and organization - Design improved context schema 2. **Session Management** - Create session tracking mechanism - Implement context association logic - Build session persistence 3. **Improved UX Tools** - Create context discovery tools - Implement smart recommendations - Build context search capabilities ### Phase 3: Advanced Features (6-8 weeks) 1. **Relationship Management** - Implement parent-child relationships - Add cross-references between contexts - Create context graphs 2. **AI Enhancements** - Integrate Pydantic AI (if viable) - Add intelligent context suggestions - Implement semantic search 3. **Production Hardening** - Add comprehensive error handling - Implement security features - Performance optimization ## Technical Considerations ### Pydantic Integration Pydantic will provide several benefits: - Strong type validation for MCP messages - Schema definition and enforcement - JSON serialization/deserialization - Integration with FastAPI (if used for admin interface) ### Pydantic AI Exploration Need to investigate: - Current stability and production readiness - Benefits for context schema definition - Potential for intelligent context processing - Any licensing or deployment limitations ### Storage Considerations 1. **Initial Approach** - JSON files for simplicity and continuity - Improved directory structure - Better indexing for performance 2. **Future Options** - SQLite for embedded database - PostgreSQL for larger installations - Vector database for semantic search ## Migration Strategy ### For Existing Users 1. Provide a migration tool to convert existing context files 2. Document the transition process clearly 3. Maintain backward compatibility where possible 4. Provide a clear deprecation timeline ### For New Users 1. Simplified onboarding process 2. Reduced dependency on Claude session IDs 3. Better documentation and examples 4. Improved error messages and guidance ## Success Metrics - **Usability**: Significantly reduced friction in context management - **Session Handling**: Seamless context persistence across sessions - **Performance**: Equal or better performance compared to Node.js version - **Maintainability**: Cleaner, well-documented Python codebase - **Extensibility**: Clear paths for feature additions and customization ## Conclusion The Python rewrite represents a significant opportunity to address the core UX challenges in the current implementation while improving maintainability and extensibility. By focusing on session management and context discovery, we can create a much more intuitive and useful tool that enhances Claude's capabilities without requiring users to manage technical details like session IDs.