# 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.