ML Jupyter MCP

# MCP Server Development TODOs ## Creating a Good MCP Server - Improvements for Jupyter Executor ### Current Implementation Strengths - ✅ Clear separation of concerns (daemon, client, MCP server modules) - ✅ Persistent state management via kernel daemon - ✅ Virtual environment auto-detection - ✅ Basic error handling and status reporting - ✅ Clean architecture using MCP SDK decorators ### Proposed Improvements #### 1. Enhanced Error Handling & Logging - [ ] Replace print statements with structured logging (use Python logging module) - [ ] Implement retry logic for kernel communication failures - [ ] Add detailed error messages with recovery suggestions - [ ] Create error codes for different failure scenarios - [ ] Add graceful degradation when kernel is unavailable #### 2. Resource Management - [ ] Add configurable timeout mechanisms for long-running code - [ ] Implement memory usage monitoring and limits - [ ] Add automatic kernel restart capability on critical errors - [ ] Implement resource cleanup on shutdown - [ ] Add connection pooling for multiple clients #### 3. Security Enhancements - [ ] Add code execution sandboxing options - [ ] Implement input validation for file paths - [ ] Add configurable execution limits (CPU time, memory) - [ ] Sanitize code inputs to prevent injection - [ ] Add authentication/authorization for multi-user scenarios #### 4. Feature Additions - [ ] Support for multiple concurrent kernels (kernel pool) - [ ] Kernel session management (save/restore state) - [ ] Rich output support (images, HTML, LaTeX) - [ ] Variable inspection tools (list vars, get var info) - [ ] Code completion and intellisense support - [ ] Support for different kernel types (R, Julia, etc.) - [ ] Streaming output for long-running cells - [ ] Interrupt execution capability #### 5. Configuration Improvements - [ ] Make daemon port configurable via environment variables - [ ] Add YAML/JSON configuration file support - [ ] Support custom kernel specs and configurations - [ ] Add connection timeout settings - [ ] Configurable output size limits #### 6. MCP Best Practices - [ ] Add comprehensive tool descriptions with JSON schemas - [ ] Implement progress reporting for long operations - [ ] Add cancellation support for running operations - [ ] Version the API for backward compatibility - [ ] Add health check endpoints - [ ] Implement proper async/await patterns throughout #### 7. Testing & Reliability - [ ] Create comprehensive unit test suite - [ ] Add integration tests for MCP tools - [ ] Implement health monitoring and auto-recovery - [ ] Add performance benchmarks - [ ] Create stress tests for concurrent usage #### 8. Documentation & Developer Experience - [ ] Create detailed API documentation - [ ] Add usage examples for common scenarios - [ ] Create troubleshooting guide - [ ] Add developer setup guide - [ ] Create architecture diagrams ### Implementation Priority 1. **High Priority (Core Improvements)** - Structured logging - Timeout mechanisms - Input validation - Health checks 2. **Medium Priority (Enhanced Features)** - Multiple kernel support - Rich output handling - Configuration system - Progress reporting 3. **Low Priority (Nice to Have)** - Additional kernel types - Session save/restore - Code completion - Performance optimizations ### MCP Server Best Practices Checklist - [ ] **Tool Design** - Each tool has a single, clear responsibility - Tool names are descriptive and follow naming conventions - Parameters are well-documented with types and descriptions - Return values are consistent and predictable - [ ] **Error Handling** - All exceptions are caught and handled gracefully - Error messages are helpful and actionable - Failures don't crash the entire server - Retry logic for transient failures - [ ] **Performance** - Async operations for I/O bound tasks - Efficient resource usage - Caching where appropriate - Connection pooling for external services - [ ] **Observability** - Structured logging at appropriate levels - Metrics for monitoring - Tracing for debugging - Health endpoints for monitoring - [ ] **Security** - Input validation and sanitization - Principle of least privilege - Secure communication channels - No hardcoded secrets - [ ] **Maintainability** - Clean code architecture - Comprehensive documentation - Automated testing - Version control and release management ### Notes for Implementation - Consider using `asyncio` throughout for better concurrency - Look into using `jupyter-client` async APIs - Consider implementing a plugin architecture for extensibility - Think about containerization for easier deployment - Consider adding telemetry for usage analytics (with user consent) ### References - [MCP SDK Documentation](https://modelcontextprotocol.io/docs) - [Jupyter Client Documentation](https://jupyter-client.readthedocs.io/) - [Python Logging Best Practices](https://docs.python.org/3/howto/logging.html)