Implement MCP server for AI assistant integration #14
Description
Overview
Implement Model Context Protocol (MCP) server to enable AI assistants like Claude to control Music.app through natural language.
Scope
Create complete MCP server implementation that provides music control capabilities to AI assistants with proper rate limiting and safety measures.
Implementation Requirements
MCP Server (cmd/maestro-mcp/)
- MCP protocol implementation following specification
- Music control tools and capabilities
- Rate limiting for AI client requests (20/minute)
- Stateless operation with session coordination
- Error handling and graceful degradation
- Tool discovery and capability advertisement
Music Control Tools
- play_track(track_id) - Play specific track
- control_playback(action) - Play, pause, stop, next, previous
- set_volume(level) - Control volume level
- search_music(query) - Search library content
- get_status() - Get current player status
- manage_queue(action, track_id) - Queue manipulation
- get_library_info() - Library statistics and info
MCP Protocol Implementation
- Tool definition and metadata
- Request/response handling
- Error code mapping
- Capability negotiation
- Session management coordination
- Security and validation
AI Integration Features
- Natural language to music command translation
- Context-aware music recommendations
- Playlist generation based on mood/genre
- Smart queue management
- Music discovery assistance
- Playback history and preferences
Technical Requirements
- Rate Limiting - 20 requests per minute per AI client
- Stateless Design - No persistent session state
- Error Handling - Graceful failures with helpful messages
- Security - Input validation and command sanitization
- Performance - Fast response times for AI interactions
- Compatibility - Works with Claude and other MCP clients
MCP Tools Implementation
- Comprehensive tool definitions with JSON schema
- Parameter validation and type checking
- Clear error messages for invalid requests
- Tool categorization and organization
- Usage examples and documentation
Success Criteria
- MCP server implements protocol correctly
- All music control tools function properly
- Rate limiting prevents abuse
- AI assistants can control music effectively
- Error handling provides useful feedback
- Performance meets responsiveness requirements
- Integration tests verify functionality
- Documentation enables easy AI integration
Integration Points
- Daemon for actual music control operations
- Session management for request coordination
- Application layer for business logic
- Rate limiting for client management
Testing Strategy
- Unit tests for all MCP tools and protocol handling
- Integration tests with real AI assistants
- Rate limiting verification
- Error scenario testing
- Performance testing with concurrent AI requests
- Security testing for input validation