Carla MCP Server

A comprehensive Model Context Protocol (MCP) server for complete control over the Carla audio plugin host. This server enables natural language control of professional audio production workflows through Large Language Models.

🎯 Project Overview

The Carla MCP Server provides 45 tools across 7 functional categories for complete audio production control. Built with Python 3.12+, it offers a production-ready interface between AI assistants and professional audio software.

Key Features:

• 🤖 AI-Native: Designed for natural language control through LLMs
• 🎛️ Complete Control: 45 tools covering every aspect of audio production
• ⚡ Real-Time: Low-latency monitoring and analysis capabilities
• 🔧 Professional: Production-ready with comprehensive error handling and type safety
• 📈 Scalable: Modular architecture supporting complex workflows

🌟 What You Can Do

Ask an AI assistant to help with professional audio tasks:

• "Load my mixing project and set up a drum bus with compression"
• "Create a filter sweep effect on the lead synth, sync it to tempo"
• "Analyze my mix and suggest improvements for better frequency balance"
• "Set up parallel compression for vocals with different verse/chorus settings"
• "Prepare this session for live performance with low latency"

🎛️ Complete Feature Set

🗂️ Session Management (8 tools)

• Load/save projects with auto-connection
• Create A/B snapshots for comparison
• Hot-swap between sessions with crossfading
• Export/import multiple formats
• Session cleanup and optimization

🔌 Plugin Control (8 tools)

• Load VST2/3, LV2, LADSPA, DSSI, AU, SF2/SFZ plugins
• Scan directories for available plugins
• Clone and replace plugins with parameter mapping
• Batch processing with plugin chains
• Real-time plugin state control

🔗 Audio Routing (7 tools)

• Complex audio/MIDI routing matrix
• Bus creation and management with grouping
• Sidechain configuration for compression
• Real-time routing matrix inspection

🎚️ Parameter Automation (8 tools)

• Advanced automation with multiple curve types
• MIDI CC mapping with custom ranges
• Macro controls for multiple parameters
• Direct parameter get/set operations
• Real-time parameter control and monitoring

📊 Real-Time Analysis (5 tools)

• Spectrum analysis with customizable FFT
• Audio level metering (Peak, RMS, LUFS)
• Latency measurement and optimization
• Feedback loop detection and prevention
• Parameter capture over time

🔊 JACK Integration (6 tools)

• JACK port management and connections
• System audio routing
• Port monitoring and status
• Auto-connection for plugins
• Connection stability verification

🖥️ Hardware Control (3 tools)

• Audio interface configuration
• Device discovery and management
• Control surface mapping
• Monitor calibration support

🚀 Installation

Prerequisites

1. Carla Audio Plugin Host

   # Ubuntu/Debian
   sudo apt install carla carla-dev
   
   # Or build from source for latest features
   git clone https://github.com/falkTX/Carla.git
   cd Carla
   make
   sudo make install
   

2. Python Environment

   # Requires Python 3.12+
   python3 --version  # Should be 3.12 or higher
   

3. Audio System

   # JACK Audio Connection Kit (recommended)
   sudo apt install jackd2 jack-tools
   
   # For Windows VST support on Linux
   sudo apt install wine wine32 wine64
   

Setup

1. Clone and Install

   git clone https://github.com/your-org/carla-mcp-server.git
   cd carla-mcp-server
   
   # Install dependencies
   pip install -r requirements.txt
   
   # Or install with development tools
   pip install -e ".[dev]"
   

2. Configure Environment

   # Set Carla path (adjust to your installation)
   export CARLA_PATH=/usr/share/carla
   export PYTHONPATH=$CARLA_PATH/source/frontend:$PYTHONPATH
   export LD_LIBRARY_PATH=/usr/lib/carla:$LD_LIBRARY_PATH
   

3. Test Installation

   # Run test suite
   pytest tests/
   
   # Test Carla integration
   python -c "import carla_backend; print('Carla integration working!')"
   

🎮 Usage

Quick Start

# Start the MCP server
python server.py

# Or with debugging
python server.py --debug --log-level INFO

MCP Client Integration

Add to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "carla-mcp": {
      "type": "stdio",
      "command": "python",
      "args": ["/path/to/carla-mcp-server/server.py"],
      "env": {
        "CARLA_PATH": "/usr/share/carla",
        "PYTHONPATH": "/usr/share/carla/source/frontend"
      }
    }
  }
}

Natural Language Examples

Once connected to an LLM, you can use natural language for complex audio tasks:

Basic Mixing Session

User: "Load my rock song project and add an EQ to remove vocal rumble and
improve my vocal clarity."

AI: Perfect! I've loaded your project, and optimized your microphone EQ
for professional vocal quality:

  🎯 Vocal Optimization Applied:

  ✅ Rumble Removal:
  - 50Hz: -12dB (removes sub-bass rumble)
  - 100Hz: -8dB (reduces low-frequency noise)
  - 156Hz: -6dB (cleans up muddy low-mids)

  ✅ Clarity Enhancement:
  - 2500Hz: +4dB (vocal presence)
  - 3500Hz: +6dB (speech intelligibility)
  - 5000Hz: +3dB (clarity and definition)

  ✅ Sibilance Reduction:
  - 10000Hz: -4dB (tames harsh 's' and 't' sounds)

  Your microphone signal is now routed through the optimized EQ, providing cleaner, more
  professional vocal sound with reduced rumble, enhanced clarity, and controlled sibilance. The settings follow standard pop/rock vocal processing techniques.

Creative Sound Design

User: "Create an evolving filter sweep on the synth pad that builds tension"

AI: I'll create a dynamic filter automation that builds tension:
1. Finding your synth pad plugin
2. Setting up exponential filter frequency automation
3. Adding resonance automation for more character
4. Syncing the sweep to your project tempo

Created a 16-bar filter sweep that starts subtle and builds to an
intense peak, perfect for creating tension in your arrangement.

Problem Solving

User: "Something sounds muddy in my mix and there's a slight feedback"

AI: Let me analyze and fix those issues:
1. Running spectrum analysis to identify muddy frequencies
2. Scanning for feedback loops in your routing
3. Applying corrective EQ to clear up the muddiness
4. Fixing the feedback path I found

Analysis complete! Found buildup around 200-300Hz (applied corrective EQ)
and fixed a feedback loop between your reverb send and input.
Your mix should sound much cleaner now.

Analyze A Mix's Song Structure

User: "Analyze my VU meter's levels for the duration of my song and using
the historic measurement data, identify where in the timecode each verse,
chorus, and solo section begins."

AI: Let me begin analyzing your VU meter plugin for 05:32 minutes (the exact
length of your mix, plus two seconds to accommodate for a slight delay in playback.)

...

Completed my analysis of your VU meter levels! Here is a breakdown of your song's
musical structure based on the historical measurements data I procssed:

1. 00:02 - Intro
2. 00:28 - First verse
3. 01:20 - First chorus
4. 01:50 - Second verse
5. 2:45 - Second chorus
6. 3:32 - Solo
7. 4:02 - Breakdown
8. 4:20 - Last chorus
9. 5:12 - Outro

🛠️ Development

Project Structure

carla-mcp-server/
├── server.py              # Main MCP server
├── carla_controller.py     # Carla backend wrapper
├── tool_registry.py       # Tool registration system
├── types.py               # Type definitions
├── base_tools.py          # Base tool framework
├── tools/                 # MCP tool implementations
│   ├── session_tools.py   # Session management
│   ├── plugin_tools.py    # Plugin control
│   ├── routing_tools.py   # Audio routing
│   ├── parameter_tools.py # Parameter automation
│   ├── analysis_tools.py  # Real-time analysis
│   ├── jack_tools.py      # JACK integration
│   └── hardware_tools.py  # Hardware control
├── monitors/              # Real-time monitoring
│   ├── audio_monitor.py   # Audio level monitoring
│   ├── cpu_monitor.py     # Performance monitoring
│   └── event_monitor.py   # Event streaming
├── tests/                 # Comprehensive test suite
└── config/                # Configuration files

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=carla_mcp_server --cov-report=html

# Run specific test category
pytest tests/test_server.py::TestSessionManagement

# Run integration tests (requires Carla)
pytest tests/test_complete_suite.py

Code Quality

# Format code
black carla_mcp_server/

# Sort imports
isort carla_mcp_server/

# Type checking
mypy carla_mcp_server/

# Linting
flake8 carla_mcp_server/

# Run all quality checks
pre-commit run --all-files

Adding New Tools

1. Create Tool Method in appropriate tools/ module:

   async def my_new_tool(self, param1: str, param2: int = 10) -> dict:
       """Tool description for documentation."""
       try:
           # Implementation
           return {"success": True, "result": data}
       except Exception as e:
           return {"success": False, "error": str(e)}
   

2. Register Tool in tool_registry.py:

   ToolDefinition(
       name="my_new_tool",
       description="Description for MCP clients",
       handler="tool_class_name",
       input_schema={/* JSON schema */}
   )
   

3. Add Tests in tests/:

   async def test_my_new_tool():
       # Test implementation
   

🧩 Complete Tool Reference

Session Management

• load_session - Load Carla project files
• save_session - Save current session state
• create_snapshot - Create A/B comparison snapshots
• switch_session - Hot-swap between sessions
• list_sessions - Show available sessions
• delete_session - Remove sessions
• export_session - Export to audio formats
• import_session - Import from external formats

Plugin Control

• load_plugin - Load any plugin format
• scan_plugins - Discover available plugins
• control_plugin - Activate/bypass/solo/remove
• batch_process - Apply plugin chains to audio
• list_plugins - Show loaded plugins
• get_plugin_info - Detailed plugin information
• clone_plugin - Duplicate plugins with settings
• replace_plugin - Swap plugins with parameter mapping

Audio Routing

• connect_audio - Create audio connections
• create_bus - Build audio buses for grouping
• setup_sidechain - Configure sidechain routing
• get_routing_matrix - View complete routing
• disconnect_audio - Remove connections
• create_send - Set up send/return effects
• set_connection_gain - Adjust connection levels

Parameter Automation

• automate_parameter - Create automation curves
• map_midi_cc - MIDI controller mapping
• create_macro - Multi-parameter macros
• record_automation - Capture parameter changes
• set_parameter - Set plugin parameter values directly
• get_parameter - Get plugin parameter values and info
• randomize_parameters - Creative randomization
• morph_parameters - Smooth parameter transitions

Real-Time Analysis

• analyze_spectrum - FFT spectrum analysis
• measure_levels - Peak/RMS/LUFS metering
• capture_plugin_parameters - Parameter monitoring
• detect_feedback - Feedback loop detection
• analyze_latency - System latency measurement

JACK Integration

• list_jack_ports - Show available JACK ports
• connect_jack_ports - Connect JACK ports
• disconnect_jack_ports - Disconnect JACK ports
• get_jack_connections - View port connections
• connect_system_to_plugin - Route system audio to plugins
• connect_plugin_to_system - Route plugins to system output

Hardware Control

• configure_audio_interface - Set up audio hardware
• list_audio_devices - Discover audio devices
• map_control_surface - Configure MIDI controllers

📖 Documentation

• API.md - Complete tool reference with examples
• ARCHITECTURE.md - Technical architecture documentation
• CONTRIBUTING.md - Development guidelines

🔧 Configuration

Server Configuration (config/config.yaml)

server:
  host: localhost
  port: 8765
  log_level: INFO

carla:
  path: /usr/share/carla
  wine_prefix: ~/.wine
  auto_start_engine: true

audio:
  driver: JACK
  sample_rate: 48000
  buffer_size: 512
  auto_connect: true

plugins:
  scan_paths:
    - /usr/lib/lv2
    - /usr/lib/vst
    - ~/.wine/drive_c/Program Files/Common Files/VST3
  cache_enabled: true

monitoring:
  audio_enabled: true
  cpu_enabled: true
  update_interval_ms: 100

Environment Variables

# Essential paths
export CARLA_PATH=/usr/share/carla
export PYTHONPATH=$CARLA_PATH/source/frontend:$PYTHONPATH
export LD_LIBRARY_PATH=/usr/lib/carla:$LD_LIBRARY_PATH

# Optional configuration
export CARLA_MCP_LOG_LEVEL=INFO
export CARLA_MCP_HOST=localhost
export CARLA_MCP_PORT=8765

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Quick Contribution Steps:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

This project is licensed under the GPL-2.0-or-later License - see the LICENSE file for details.

🙏 Credits

• Built on top of Carla by falkTX
• Uses the Model Context Protocol by Anthropic
• Inspired by the audio production community

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: See docs/ directory
• Community: Join the Carla Discord community

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Ready to revolutionize your audio production workflow with AI assistance? Get started today! 🎵✨