Katamari MCP Server

# Katamari MCP - Stretch Goals TODO ## High Priority Stretch Goals ### 1. ~~Named Pipe Communication for MCP Stdio Mode~~ ~~(DEPRECATED - stdio removed)~~ ~~**Goal**: Replace stdio with named pipes for faster startup and persistent context~~ ~~**Benefits**~~: ~~- Reduced startup time (no process spawning overhead)~~ ~~- Persistent context between calls~~ ~~- Better resource utilization~~ ~~- Enables stateful conversations~~ ~~**Implementation Plan**~~: ~~- [ ] Research named pipe implementation for cross-platform support~~ ~~- [ ] Create pipe-based transport layer in `katamari_mcp/transport/`~~ ~~- [ ] Implement lightweight pipe wrapper for client interaction~~ ~~- [ ] Add fallback to stdio for compatibility~~ ~~- [ ] Test performance improvements~~ **Note**: stdio transport has been removed. Named pipe communication is no longer relevant. **Files to Create/Modify**: ~~- `katamari_mcp/transport/named_pipe.py` - New named pipe transport~~ ~~- `katamari_mcp/transport/pipe_wrapper.py` - Lightweight client wrapper~~ ~~- `katamari_mcp/server.py` - Add named pipe server mode~~ ~~- `scripts/pipe_client.py` - Simple client for pipe interaction~~ **Note**: Named pipe implementation removed as it depends on stdio transport. ### 2. MCP TaskMaster with Stateful Background Tasks **Goal**: Stateful task management system for long-running operations **Use Cases**: - `katamari add 'foo'` - Start background task - `katamari status 'foo'` - Check task completion - `katamari list` - Show all background tasks - `katamari cancel 'foo'` - Cancel running task **Features**: - Persistent task storage (SQLite/JSON) - Task lifecycle management (pending, running, completed, failed) - Progress reporting and logs - Manual intervention handling - Task dependencies and workflows **Implementation Plan**: - [ ] Design task data models and state machine - [ ] Create TaskMaster service in `katamari_mcp/taskmaster/` - [ ] Implement persistent task storage - [ ] Add task management MCP endpoints - [ ] Create CLI interface for task interaction - [ ] Add task monitoring and alerting **Files to Create/Modify**: - `katamari_mcp/taskmaster/__init__.py` - `katamari_mcp/taskmaster/taskmaster.py` - Core task management - `katamari_mcp/taskmaster/models.py` - Task data models - `katamari_mcp/taskmaster/storage.py` - Persistent storage - `katamari_mcp/taskmaster/cli.py` - Command-line interface - `katamari_mcp/server.py` - Add TaskMaster endpoints ## Medium Priority Stretch Goals ### 3. Enhanced Context Management **Goal**: Build and maintain conversation context across calls **Features**: - Session-based context storage - Context compression and summarization - Cross-session context persistence - Intelligent context retrieval ### 4. Performance Optimization Suite **Goal**: Comprehensive performance monitoring and optimization **Features**: - Real-time performance metrics - Automatic performance tuning - Resource usage optimization - Bottleneck identification and resolution ### 5. Advanced Security Features **Goal**: Enhanced security for production deployments **Features**: - Capability sandboxing improvements - Access control and permissions - Audit logging and compliance - Security scanning and validation - **Data Exposure Heuristic** - New heuristic tag for information leakage risk assessment **Data Exposure Heuristic Implementation**: - **New Heuristic Tag**: `DataExposure` enum with levels: NONE, LOCAL, INTERNAL, EXTERNAL, EXFILTRATION - **Use Case**: Capabilities like 'facter' that expose local system details and could be exfiltrated - **Risk Assessment**: Automatically detect capabilities with high data leakage potential - **Safety Controls**: Require additional approval/testing for high-exposure capabilities - **Integration**: Extend existing 6-tag heuristic system to include information leakage assessment **Implementation Plan**: - [ ] Add `DataExposure` enum to `katamari_mcp/acp/heuristics.py` - [ ] Implement data exposure assessment logic in heuristic engine - [ ] Update heuristic rules to consider data exposure in approval decisions - [ ] Add tests for data exposure heuristic functionality - [ ] Update documentation and examples ## Medium Priority Stretch Goals ### 6. Skills Integration System **Goal**: Import and integrate Claude Skills and OpenSkills into Katamari's ecosystem **Use Cases**: - `katamari import-skill claude://skill-name` - Import Claude Skill - `katamari import-skill openskill://skill-id` - Import OpenSkill - `katamari list-skills` - Show available imported skills - `katamari convert-skill skill-name` - Convert to Katamari capability **Features**: - Automatic skill discovery and parsing - Skill format conversion (Claude/OpenSkills → Katamari capability) - Dependency resolution and validation - Skill metadata and documentation import - Version compatibility checking - Skill testing and validation **Implementation Plan**: - [ ] Research Claude Skills and OpenSkills formats - [ ] Create skill parser modules in `katamari_mcp/skills/` - [ ] Implement skill-to-capability converter - [ ] Add skill import CLI commands - [ ] Create skill validation and testing framework - [ ] Build skill registry and metadata management **Files to Create/Modify**: - `katamari_mcp/skills/__init__.py` - `katamari_mcp/skills/claude_parser.py` - Claude Skills format parser - `katamari_mcp/skills/openskills_parser.py` - OpenSkills format parser - `katamari_mcp/skills/converter.py` - Skill to capability converter - `katamari_mcp/skills/registry.py` - Skill registry and metadata - `katamari_mcp/skills/validator.py` - Skill validation framework - `katamari_mcp/devtools/cli.py` - Add skill import commands ## Low Priority Stretch Goals ### 7. Plugin Marketplace Integration **Goal**: Discover and install community capabilities **Features**: - Capability registry and discovery - Automated installation and updates - Version management and compatibility - Community ratings and reviews ### 8. Advanced Analytics Dashboard **Goal**: Web-based dashboard for system monitoring **Features**: - Real-time system metrics - Interactive performance graphs - Task management interface - Configuration management ### 9. Multi-Modal Capabilities **Goal**: Support for image, audio, and video processing **Features**: - Image analysis and generation - Audio processing and synthesis - Video analysis and manipulation - Multi-modal workflow composition ## Implementation Notes ### Skills Integration Technical Considerations **Claude Skills Format**: - JSON-based skill definitions - Tool specifications and parameters - Natural language descriptions - Version and dependency metadata **OpenSkills Format**: - YAML/JSON skill manifests - Standardized skill interface - Community-maintained registry - Semantic versioning **Conversion Process**: 1. Parse source skill format 2. Extract tool definitions and logic 3. Convert to Katamari capability structure 4. Generate Python code with proper imports 5. Add validation and error handling 6. Create test suite automatically 7. Generate documentation **Validation**: - Security scanning of imported skills - Dependency compatibility checking - Code quality validation - Performance benchmarking - Integration testing ### Named Pipes Technical Considerations **Windows**: `\\.\pipe\katamari_mcp` **Linux/macOS**: `/tmp/katamari_mcp.sock` or `/tmp/katamari_mcp.pipe` **Security**: - Pipe permissions and access control - Authentication for pipe connections - Secure data transmission **Error Handling**: - Pipe disconnection detection - Automatic reconnection - ~~Graceful fallback to stdio~~ ~~(REMOVED)~~ ### TaskMaster Technical Considerations **Storage Options**: - SQLite for structured data - JSON file for simplicity - Redis for distributed systems **Task Types**: - One-shot tasks (single execution) - Recurring tasks (scheduled) - Workflow tasks (multi-step) - Interactive tasks (requires user input) **State Management**: - Task state persistence - Recovery from crashes - Concurrent task handling - Resource allocation ## Integration with Existing Architecture ### Phase 3 Integration - TaskMaster can leverage Workflow Optimizer for complex tasks - Predictive Engine can forecast task completion times - Self-Healing can recover failed tasks - Knowledge Transfer can optimize task execution ### ACP Integration - TaskMaster can be managed by ACP for self-optimization - Tasks can generate new capabilities automatically - Learning from task execution patterns - Adaptive task scheduling based on performance ## Testing Strategy ### Skills Integration Testing - Skill format parsing tests - Conversion accuracy tests - Import validation tests - Generated capability tests - CLI interface tests ### ~~Named Pipes Testing~~ ~~(DEPRECATED)~~ ~~- Cross-platform compatibility tests~~ ~~- Performance benchmarking vs stdio~~ ~~- Concurrent connection handling~~ ~~- Error recovery testing~~ ### TaskMaster Testing - Task lifecycle management tests - Persistence and recovery tests - Concurrent task execution tests - CLI interface tests ## Documentation Updates ### User Documentation - Named pipe setup guide - TaskMaster usage examples - Skills import guide - CLI reference documentation - ~~Migration guide from stdio~~ ~~(REMOVED)~~ ### Developer Documentation - Transport layer architecture - TaskMaster API reference - Skills integration guide - Plugin development guide - Performance optimization guide ## Success Metrics ### Named Pipes - Startup time reduction > 50% - Memory usage reduction > 20% - User satisfaction improvement - Zero compatibility issues ### TaskMaster - Task completion rate > 95% - Average task response time < 100ms - System uptime > 99.9% - User adoption rate > 80% ### Skills Integration - Skill import success rate > 90% - Conversion accuracy > 95% - Imported skill compatibility > 85% - Community skill adoption rate > 60% --- **Note**: These stretch goals are designed to build upon the existing Phase 3 Advanced Agency architecture while maintaining backward compatibility and system reliability.