Carla MCP Server

# Carla MCP Server Architecture Comprehensive architectural documentation for the Carla MCP Server project. ## Project Overview The Carla MCP Server is a production-ready Model Context Protocol (MCP) server that provides comprehensive control over the Carla audio plugin host. Built with Python 3.12+, it offers 45+ tools across 7 functional categories for professional audio production workflows. **Key Metrics:** - **Codebase Size**: ~6,500 lines of Python code - **Tool Count**: 45+ implemented methods across 7 categories - **Test Coverage**: 660+ lines of comprehensive test suite - **Architecture**: Modular design with clear separation of concerns ## Directory Structure ``` carla-mcp-server/ ├── 📁 config/ # Configuration files │ └── config.yaml # Server configuration ├── 📁 docs/ # Documentation ├── 📁 monitors/ # Real-time monitoring (310 lines) │ ├── audio_monitor.py # Audio level monitoring (108 lines) │ ├── cpu_monitor.py # Performance monitoring (128 lines) │ └── event_monitor.py # Event streaming (74 lines) ├── 📁 tests/ # Test suite (660+ lines) │ ├── test_complete_suite.py # Comprehensive integration tests (468 lines) │ └── test_server.py # Unit tests (192 lines) ├── 📁 tools/ # MCP tool implementations (2,900+ lines) │ ├── analysis_tools.py # Real-time analysis (534 lines) │ ├── hardware_tools.py # Hardware interface (235 lines) │ ├── jack_tools.py # JACK integration (394 lines) │ ├── parameter_tools.py # Parameter automation (740 lines) │ ├── plugin_tools.py # Plugin control (689 lines) │ ├── routing_tools.py # Audio routing (558 lines) │ └── session_tools.py # Session management (567 lines) ├── 📄 base_tools.py # Base tool framework (355 lines) ├── 📄 carla_controller.py # Carla backend wrapper (800 lines) ├── 📄 main.py # Entry point (6 lines) ├── 📄 server.py # Main MCP server (701 lines) ├── 📄 tool_registry.py # Tool registration system (569 lines) ├── 📄 types.py # Type definitions (263 lines) ├── 📄 pyproject.toml # Python project configuration ├── 📄 requirements.txt # Dependencies ├── 📄 .mcp.json # MCP client configuration └── 📄 README.md # Main documentation ``` ## Core Architecture Components ### 1. MCP Server Core (`server.py` - 701 lines) The main MCP server implementation that orchestrates all functionality. **Key Classes:** - `CarlaMCPServer`: Main server class with tool routing and lifecycle management - Tool registration and execution framework - WebSocket/stdio communication handling - Error handling and logging **Responsibilities:** - MCP protocol implementation - Tool discovery and registration - Request routing and response handling - Session lifecycle management - Performance monitoring integration ### 2. Carla Controller (`carla_controller.py` - 800 lines) High-level wrapper for Carla backend operations, providing a clean Python API. **Key Classes:** - `CarlaController`: Main interface to Carla engine - Plugin type enumerations (`PluginType`, `BinaryType`) - Engine state management - Audio/MIDI port handling **Responsibilities:** - Carla engine lifecycle (start/stop) - Plugin loading and management - Audio routing and connections - Parameter control and automation - Project file operations ### 3. Tool Registry System (`tool_registry.py` - 569 lines) Clean registration system for MCP tools with automatic schema generation. **Key Classes:** - `ToolDefinition`: Structured tool metadata - `MCPToolRegistry`: Registry with schema generation - `create_carla_tool_registry()`: Factory function **Features:** - Automatic MCP tool schema generation - Tool categorization and versioning - Handler mapping and resolution - Deprecation support ### 4. Type System (`types.py` - 263 lines) Comprehensive type definitions for type safety and documentation. **Key Components:** - Type aliases: `PluginId`, `ParameterId`, `JsonDict`, etc. - Enums: `PluginType`, `BinaryType`, `ProcessMode` - Data classes: `PluginInfo`, `SessionInfo`, `PerformanceMetrics` - Protocols: `ToolHandler`, `CarlaController` - Custom exceptions: `PluginError`, `SessionError`, etc. ### 5. Base Tool Framework (`base_tools.py` - 355 lines) Foundation framework for all tool implementations. **Key Features:** - Abstract base classes for tool handlers - Common error handling patterns - Validation utilities - Response formatting standards ## Tool Implementation Architecture ### Tool Categories and Implementation Each tool category is implemented as a separate module with consistent patterns: #### 1. Session Management (`session_tools.py` - 567 lines) - **Methods**: 8 (load_session, save_session, create_snapshot, switch_session, etc.) - **Focus**: Project lifecycle and state management - **Dependencies**: File I/O, Carla project format #### 2. Plugin Control (`plugin_tools.py` - 689 lines) - **Methods**: 8 (load_plugin, scan_plugins, control_plugin, batch_process, etc.) - **Focus**: Plugin loading, control, and processing - **Dependencies**: Carla plugin API, file system scanning #### 3. Audio Routing (`routing_tools.py` - 558 lines) - **Methods**: 8 (connect_audio, create_bus, setup_sidechain, etc.) - **Focus**: Audio connections and routing matrix - **Dependencies**: Carla patchbay, JACK integration #### 4. Parameter Automation (`parameter_tools.py` - 740 lines) - **Methods**: 8 (automate_parameter, map_midi_cc, create_macro, etc.) - **Focus**: Parameter control and automation - **Dependencies**: MIDI handling, automation curves #### 5. Real-Time Analysis (`analysis_tools.py` - 534 lines) - **Methods**: 5 (analyze_spectrum, measure_levels, detect_feedback, etc.) - **Focus**: Audio analysis and measurement - **Dependencies**: NumPy, SciPy for signal processing #### 6. JACK Integration (`jack_tools.py` - 394 lines) - **Methods**: 6 (list_jack_ports, connect_jack_ports, etc.) - **Focus**: JACK audio system integration - **Dependencies**: JACK client library #### 7. Hardware Control (`hardware_tools.py` - 235 lines) - **Methods**: 3+ (configure_audio_interface, list_audio_devices, etc.) - **Focus**: Audio hardware configuration - **Dependencies**: System audio drivers ### Tool Implementation Pattern All tool classes follow a consistent pattern: ```python class ToolClass: def __init__(self, carla_controller): self.carla = carla_controller async def execute(self, tool_name: str, arguments: dict) -> dict: """Main execution entry point""" # Route to specific method based on tool_name async def specific_tool_method(self, param1: str, param2: int, **kwargs) -> dict: """Individual tool implementation""" try: # 1. Validate parameters # 2. Execute operation via carla_controller # 3. Format response return {"success": True, "data": result} except Exception as e: return {"success": False, "error": str(e)} ``` ## Monitoring Architecture ### Real-Time Monitoring System The monitoring system provides real-time insights into system performance and audio metrics: #### 1. Audio Monitor (`audio_monitor.py` - 108 lines) - Level metering (Peak, RMS, LUFS) - Spectrum analysis - Audio dropout detection - Signal quality metrics #### 2. CPU Monitor (`cpu_monitor.py` - 128 lines) - System CPU usage tracking - Plugin-specific CPU monitoring - Memory usage statistics - Performance bottleneck detection #### 3. Event Monitor (`event_monitor.py` - 74 lines) - Real-time event streaming - MIDI event capture - Parameter change tracking - Session state notifications ## Configuration Architecture ### Configuration Hierarchy 1. **Environment Variables** - `CARLA_PATH`: Carla installation path - `PYTHONPATH`: Python module resolution - `LD_LIBRARY_PATH`: Shared library resolution 2. **MCP Configuration** (`.mcp.json`) - MCP client connection settings - Server execution parameters - Environment variable definitions 3. **Application Configuration** (`config/config.yaml`) - Server host/port settings - Carla-specific configuration - Audio driver preferences - Plugin scan paths 4. **Project Configuration** (`pyproject.toml`) - Python dependencies - Development tool configuration - Build settings ## Testing Architecture ### Test Strategy The testing architecture ensures reliability across all components: #### 1. Unit Tests (`test_server.py` - 192 lines) - Individual tool method testing - Mock Carla controller for isolated testing - Parameter validation testing - Error condition coverage #### 2. Integration Tests (`test_complete_suite.py` - 468 lines) - End-to-end workflow testing - Real Carla engine integration - Multi-tool interaction scenarios - Performance regression testing #### 3. Test Organization - Async test support with pytest-asyncio - Comprehensive fixture setup - Mock strategies for external dependencies - Continuous integration ready ## Error Handling Architecture ### Error Management Strategy Consistent error handling across all components: 1. **Exception Hierarchy** - Base exceptions in `types.py` - Component-specific exceptions - Error context preservation 2. **Error Response Format** ```json { "success": false, "error": "ErrorType", "message": "Human-readable description", "details": {"additional": "context"} } ``` 3. **Logging Strategy** - Structured logging with context - Performance metrics logging - Error correlation tracking ## Dependency Architecture ### Core Dependencies #### Production Dependencies - **mcp**: Model Context Protocol implementation - **PyQt5**: Carla GUI integration - **numpy**: Numerical computations for audio analysis - **scipy**: Signal processing algorithms - **psutil**: System performance monitoring - **asyncio-mqtt**: MQTT event streaming - **aiofiles**: Async file operations - **pyyaml**: Configuration file parsing #### Development Dependencies - **pytest**: Testing framework with async support - **black**: Code formatting - **isort**: Import sorting - **flake8**: Linting - **mypy**: Type checking - **pre-commit**: Git hooks ### External System Dependencies 1. **Carla Audio Plugin Host** - Python bindings required - Plugin format support (VST2/3, LV2, etc.) - JACK audio system integration 2. **JACK Audio Connection Kit** - Low-latency audio routing - Real-time audio processing - Cross-application audio connections 3. **Audio Plugins** - VST2/VST3 support - LV2 plugin format - LADSPA/DSSI compatibility - AU support (macOS) ## Performance Architecture ### Performance Characteristics 1. **Tool Execution Performance** - Async operation design - Non-blocking audio operations - Efficient parameter caching 2. **Real-Time Constraints** - Audio thread safety - Low-latency monitoring - Minimal audio dropouts 3. **Memory Management** - Plugin state caching - Session snapshot efficiency - Resource cleanup patterns 4. **Scalability Considerations** - Multiple session support - Plugin instance limits - Connection matrix efficiency ## Security Architecture ### Security Considerations 1. **File System Access** - Path validation and sanitization - Plugin loading security - Project file integrity 2. **Network Security** - MCP protocol security - Client authentication support - Rate limiting mechanisms 3. **Process Isolation** - Plugin sandboxing via Carla - Resource limit enforcement - Crash recovery mechanisms ## Development Workflow ### Development Process 1. **Code Quality Gates** - Type checking with mypy - Code formatting with black - Import sorting with isort - Linting with flake8 2. **Testing Requirements** - Unit test coverage - Integration test validation - Performance regression testing 3. **Documentation Standards** - Comprehensive API documentation - Architecture documentation - Usage examples and tutorials ## Future Architecture Considerations ### Extensibility Design 1. **Plugin Architecture** - Tool plugin system - Custom analysis modules - Third-party integrations 2. **Protocol Extensions** - Additional MCP capabilities - Streaming protocol support - Real-time collaboration features 3. **Performance Optimization** - GPU acceleration for analysis - Distributed processing support - Advanced caching strategies --- This architecture supports professional audio production workflows while maintaining code quality, performance, and extensibility for future enhancements.