Katamari MCP Server

# Katamari MCP Implementation Roadmap ## Overview This document catalogs all implementation efforts required to complete the Katamari MCP system. Each effort includes scope, complexity, dependencies, and estimated effort. --- ## 🚨 **CRITICAL PRIORITY - Core Infrastructure** ### 1. Transport Layer Implementation **Status**: 20% Complete | **Effort**: 3-4 weeks | **Complexity**: High #### Scope - Complete WebSocket transport with proper connection management - Implement Server-Sent Events (SSE) transport - ~~Complete stdio transport with proper stdin handling~~ ~~(REMOVED)~~ - ~~Add named pipe transport (stretch goal)~~ ~~(REMOVED - depends on stdio)~~ #### Required Tasks 1. **WebSocket Transport** (`transport/websocket.py`) - [ ] Implement connection pooling and reconnection logic - [ ] Add proper message framing and validation - [ ] Implement error handling and recovery - [ ] Add authentication and security layers - [ ] Add connection state management 2. **SSE Transport** (`transport/sse.py`) - [ ] Replace placeholder error handling - [ ] Implement proper event stream management - [ ] Add client disconnection handling - [ ] Add backpressure and flow control 3. ~~**Stdio Transport** (`transport/stdio.py`)~~ ~~(REMOVED)~~ ~~- [ ] Fix stdin handling (lines 35, 38)~~ ~~- [ ] Add proper message parsing~~ ~~- [ ] Implement graceful shutdown~~ ~~- [ ] Add signal handling~~ 4. ~~**Named Pipe Transport** (`transport/named_pipe.py`)~~ ~~(REMOVED - depends on stdio)~~ ~~- [ ] Complete implementation from scratch~~ ~~- [ ] Add cross-platform pipe creation~~ ~~- [ ] Implement security and permissions~~ #### Dependencies - Base transport class completion - Error handling system (✅ complete) - Configuration system (✅ complete) #### Acceptance Criteria - All transports pass comprehensive integration tests - Support for concurrent connections - Proper error recovery and reconnection - Security validation for all transports --- ### 2. ACP Controller - Real Code Generation **Status**: 30% Complete | **Effort**: 4-6 weeks | **Complexity**: Very High #### Scope Replace template generation with actual LLM-powered code generation and integration #### Required Tasks 1. **LLM Integration** (`acp/controller.py:199-273`) - [ ] Integrate with actual LLM API (OpenAI/Claude/etc.) - [ ] Implement prompt engineering for code generation - [ ] Add context-aware generation based on existing codebase - [ ] Implement code validation and safety checks 2. **Code Generation Pipeline** - [ ] Replace `_generate_capability_code()` template (line 199) - [ ] Implement multi-pass generation (draft → review → refine) - [ ] Add dependency analysis and resolution - [ ] Implement code formatting and linting 3. **Integration System** - [ ] Replace `_integrate_capability()` placeholder (line 241) - [ ] Implement actual file system integration - [ ] Add import and dependency management - [ ] Implement rollback mechanisms 4. **Fix Generation System** - [ ] Replace `_generate_fix()` placeholder (line 273) - [ ] Implement error analysis and fix generation - [ ] Add fix validation and testing - [ ] Implement progressive fix application #### Dependencies - LLM API access and configuration - Code analysis and parsing tools - File system management - Security validation system (✅ complete) #### Acceptance Criteria - Can generate functional Python code from requirements - Generated code passes security validation - Integration works without breaking existing functionality - Fixes are generated and applied successfully --- ### 3. Intelligent Router - LLM Integration **Status**: 35% Complete | **Effort**: 2-3 weeks | **Complexity**: High #### Scope Add actual LLM-powered intelligent routing instead of fallback to simple routing #### Required Tasks 1. **LLM Routing Engine** (`router/intelligent_router.py:303-330`) - [ ] Implement LLM API integration for routing decisions - [ ] Add context-aware routing based on request analysis - [ ] Implement capability matching and scoring - [ ] Add routing optimization and learning 2. **ACP Feedback Integration** - [ ] Replace placeholder ACP endpoints (lines 303, 309, 315, 319) - [ ] Implement real feedback collection from ACP systems - [ ] Add feedback analysis for routing improvement - [ ] Implement adaptive routing based on feedback 3. **Error Formatting and Recovery** - [ ] Replace placeholder error formatting (line 330) - [ ] Implement intelligent error analysis - [ ] Add automatic retry with different routing - [ ] Implement fallback strategies #### Dependencies - LLM API integration - ACP feedback system completion - Error handling system (✅ complete) #### Acceptance Criteria - Routing decisions are context-aware and intelligent - System learns from feedback and improves over time - Error handling is intelligent and adaptive - Performance meets or exceeds simple routing --- ## 🔧 **HIGH PRIORITY - Core ACP Systems** ### 4. Feedback System - Analysis & Processing **Status**: 40% Complete | **Effort**: 2-3 weeks | **Complexity**: Medium #### Scope Complete feedback system with actual analysis and processing capabilities #### Required Tasks 1. **User Satisfaction Analysis** (`acp/feedback.py:373`) - [ ] Replace empty `_update_user_satisfaction()` method - [ ] Implement sentiment analysis on user feedback - [ ] Add satisfaction scoring algorithms - [ ] Implement trend analysis and detection 2. **Performance Metrics Processing** (`acp/feedback.py:379`) - [ ] Replace empty `_update_performance_metrics()` method - [ ] Implement performance trend analysis - [ ] Add anomaly detection in performance data - [ ] Implement metric correlation and causation analysis 3. **Error Pattern Analysis** (`acp/feedback.py:385`) - [ ] Replace empty `_analyze_error_patterns()` method - [ ] Implement error clustering and pattern recognition - [ ] Add root cause analysis for recurring errors - [ ] Implement predictive error prevention #### Dependencies - Data collection system (✅ complete) - Statistical analysis libraries - Machine learning for pattern recognition #### Acceptance Criteria - User satisfaction is accurately measured and tracked - Performance metrics provide actionable insights - Error patterns are identified and prevented proactively --- ### 5. Development Tools - Real Capability Logic **Status**: 25% Complete | **Effort**: 3-4 weeks | **Complexity**: Medium #### Scope Replace CLI placeholders with actual capability development and validation logic #### Required Tasks 1. **Capability Development Logic** (`devtools/cli.py:358`) - [ ] Replace "TODO: Implement your capability logic here" - [ ] Implement interactive capability development - [ ] Add code generation and scaffolding - [ ] Implement testing integration 2. **Advanced Processing Logic** (`devtools/cli.py:454`) - [ ] Replace "TODO: Implement advanced processing logic" - [ ] Implement data processing pipelines - [ ] Add optimization and profiling tools - [ ] Implement performance analysis 3. **Validation Logic** (`devtools/cli.py:468`) - [ ] Replace "TODO: Implement validation logic" - [ ] Implement comprehensive capability validation - [ ] Add security and performance validation - [ ] Implement integration testing #### Dependencies - Code generation system - Testing framework completion - Security validation system (✅ complete) #### Acceptance Criteria - CLI can develop complete capabilities from scratch - Generated capabilities pass all validation checks - Development workflow is efficient and user-friendly --- ## 🔧 **MEDIUM PRIORITY - Enhancement Systems** ### 6. Performance Tracker - Complete Monitoring **Status**: 70% Complete | **Effort**: 1-2 weeks | **Complexity**: Medium #### Scope Complete performance monitoring with comprehensive metrics and analysis #### Required Tasks 1. **Execution Monitoring** (`acp/performance_tracker.py:336`) - [ ] Complete `_monitor_execution()` implementation - [ ] Add detailed performance metrics collection - [ ] Implement real-time monitoring dashboards - [ ] Add performance alerting and notifications 2. **Advanced Analytics** - [ ] Implement performance trend analysis - [ ] Add bottleneck detection and optimization suggestions - [ ] Implement resource utilization monitoring - [ ] Add predictive performance analysis #### Dependencies - Monitoring infrastructure - Analytics and visualization tools #### Acceptance Criteria - All performance metrics are accurately tracked - Performance issues are identified and resolved proactively - System provides actionable optimization insights --- ### 7. Heuristics Engine - Dynamic Adaptation **Status**: 60% Complete | **Effort**: 1-2 weeks | **Complexity**: Medium #### Scope Add dynamic weight updates and adaptive learning to heuristics system #### Required Tasks 1. **Dynamic Weight Updates** (`acp/heuristics.py:296`) - [ ] Replace placeholder `update_tag_weight()` method - [ ] Implement machine learning for weight optimization - [ ] Add feedback-based weight adjustment - [ ] Implement A/B testing for heuristics 2. **Adaptive Learning** - [ ] Implement online learning from system performance - [ ] Add context-aware heuristics adjustment - [ ] Implement heuristics evolution and optimization - [ ] Add heuristics validation and testing #### Dependencies - Machine learning framework - Feedback system completion - Performance tracking completion #### Acceptance Criteria - Heuristics weights adapt based on system performance - System learns and improves over time - Heuristics provide optimal decision-making --- ### 8. Parallel Testing - Real Test Execution **Status**: 30% Complete | **Effort**: 2-3 weeks | **Complexity**: Medium #### Scope Replace simulated testing with actual parallel test execution #### Required Tasks 1. **Real Test Execution** (`acp/testing.py:310-328`) - [ ] Replace simulated test methods with real execution - [ ] Implement parallel test orchestration - [ ] Add test isolation and resource management - [ ] Implement test result collection and analysis 2. **Advanced Testing Features** - [ ] Implement test dependency management - [ ] Add test prioritization and optimization - [ ] Implement test failure analysis and reporting - [ ] Add test performance monitoring #### Dependencies - Test execution framework - Parallel processing infrastructure - Resource management system #### Acceptance Criteria - Tests execute in parallel with proper isolation - Test results are accurate and comprehensive - System provides actionable test insights --- ## 🎯 **LOW PRIORITY - Stretch Goals** ### 9. TaskMaster System **Status**: 0% Complete | **Effort**: 3-4 weeks | **Complexity**: High #### Scope Implement TaskMaster for stateful background task management #### Required Tasks - [ ] Design task persistence and state management - [ ] Implement task scheduling and prioritization - [ ] Add task monitoring and alerting - [ ] Implement task dependency management - [ ] Add task retry and recovery mechanisms #### Dependencies - Database or persistence layer - Task queue system - Monitoring infrastructure --- ### 10. Skills Integration System **Status**: 0% Complete | **Effort**: 4-6 weeks | **Complexity**: Very High #### Scope Implement skills system for external capability integration #### Required Tasks - [ ] Design skills API and plugin architecture - [ ] Implement skill discovery and registration - [ ] Add skill validation and security checks - [ ] Implement skill marketplace and distribution - [ ] Add skill versioning and updates #### Dependencies - Plugin architecture - Security validation system (✅ complete) - Package management system --- ### 11. Advanced Analytics Dashboard **Status**: 0% Complete | **Effort**: 3-4 weeks | **Complexity**: High #### Scope Implement comprehensive analytics dashboard for system monitoring #### Required Tasks - [ ] Design dashboard architecture and UI - [ ] Implement real-time data visualization - [ ] Add customizable metrics and alerts - [ ] Implement historical data analysis - [ ] Add reporting and export capabilities #### Dependencies - Data collection and storage - Visualization framework - Real-time data streaming --- ## 📊 **Implementation Priority Matrix** | Priority | Component | Effort | Dependencies | Business Value | |----------|------------|---------|--------------|----------------| | 🚨 Critical | Transport Layer | 3-4 weeks | Low | High | | 🚨 Critical | ACP Controller | 4-6 weeks | High | Very High | | 🚨 Critical | Intelligent Router | 2-3 weeks | Medium | High | | 🔧 High | Feedback System | 2-3 weeks | Medium | High | | 🔧 High | Development Tools | 3-4 weeks | Medium | High | | 🔧 Medium | Performance Tracker | 1-2 weeks | Low | Medium | | 🔧 Medium | Heuristics Engine | 1-2 weeks | Medium | Medium | | 🔧 Medium | Parallel Testing | 2-3 weeks | Low | Medium | | 🎯 Low | TaskMaster | 3-4 weeks | High | Low | | 🎯 Low | Skills Integration | 4-6 weeks | High | Low | | 🎯 Low | Analytics Dashboard | 3-4 weeks | Medium | Low | --- ## 🗓️ **Recommended Implementation Timeline** ### **Phase 1: Core Infrastructure (8-10 weeks)** 1. **Weeks 1-4**: Transport Layer Implementation 2. **Weeks 3-8**: ACP Controller - Real Code Generation (overlap with Transport) 3. **Weeks 7-10**: Intelligent Router - LLM Integration ### **Phase 2: Core ACP Systems (4-6 weeks)** 1. **Weeks 11-13**: Feedback System - Analysis & Processing 2. **Weeks 14-17**: Development Tools - Real Capability Logic ### **Phase 3: Enhancement Systems (4-7 weeks)** 1. **Weeks 18-19**: Performance Tracker - Complete Monitoring 2. **Weeks 18-19**: Heuristics Engine - Dynamic Adaptation 3. **Weeks 20-22**: Parallel Testing - Real Test Execution ### **Phase 4: Stretch Goals (10-14 weeks)** 1. **Weeks 23-26**: TaskMaster System 2. **Weeks 27-32**: Skills Integration System 3. **Weeks 29-32**: Advanced Analytics Dashboard --- ## 🎯 **Success Metrics** ### **Technical Metrics** - Code coverage > 90% - All transports pass integration tests - LLM integration success rate > 95% - System performance meets benchmarks ### **Functional Metrics** - End-to-end capability development workflow - Autonomous system improvement and adaptation - Real-time monitoring and alerting - User satisfaction > 4.5/5 ### **Business Metrics** - Development time reduction > 50% - System reliability > 99.9% - User adoption and engagement - Community contribution and growth --- ## 📝 **Notes & Considerations** ### **Risk Mitigation** - Start with transport layer to enable proper testing - Implement comprehensive error handling early - Use phased rollout for LLM integration - Maintain backward compatibility throughout ### **Resource Requirements** - LLM API access and budget - Development and testing infrastructure - Code review and quality assurance - Documentation and training materials ### **Quality Assurance** - Comprehensive testing at each phase - Security validation for all components - Performance benchmarking and optimization - User feedback and iteration --- *This roadmap is a living document and will be updated as implementation progresses and requirements evolve.*