🎬 resolve-mcp-server

Connect AI agents to DaVinci Resolve Studio.

resolve-mcp-server is a local Model Context Protocol (MCP) server built with the MCP Python SDK that wraps the DaVinci Resolve Studio Python Scripting API. It allows MCP-compatible AI assistants to inspect your active project and timeline, read clip metadata, add markers, manage media pool folders, and automate post-production workflows using natural language.

✨ Features

• Context-Aware Inspection: Query the active project, list timelines, inspect track counts, and read clip properties and frame ranges across video, audio, and subtitle tracks.

• Timeline Automation: Create new timelines, switch active timelines, and add color-coded timeline markers with custom notes and durations at exact frame offsets.

• Media Pool Management: Inspect bins, list imported media clips, import new media files from disk, and create timelines directly from selected media pool clips.

• Render Queue Control: Load render presets, create render jobs, and trigger or check render status directly from your AI assistant.

• Local & Secure: Runs entirely on your machine over standard I/O (stdio), keeping your project data local unless queried by your client.

Related MCP server: davinci-resolve-mcp

📋 Prerequisites

1. DaVinci Resolve Studio 18+ (The free edition of DaVinci Resolve does not support external Python API scripting).

2. Python 3.10+ with the MCP Python SDK Documentation.

3. External Scripting Enabled inside DaVinci Resolve Studio:

   • Open DaVinci Resolve > Preferences > System > General

   • Set External scripting using to Local

   • Restart DaVinci Resolve Studio and open a project.

🚀 Installation

1. Clone the repository from GitHub:

   git clone https://github.com/arham837/resolve-mcp-server.git
   cd resolve-mcp-server

2. Create a virtual environment and install the package:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e ".[dev]"

3. (Optional) Override environment variables if DaVinci Resolve Studio is installed in a non-standard path:

   • macOS (Default):

     • RESOLVE_SCRIPT_API="/Library/Application Support/Blackmagic Design/DaVinci Resolve/Developer/Scripting"

     • RESOLVE_SCRIPT_LIB="/Applications/DaVinci Resolve/DaVinci Resolve.app/Contents/Libraries/Fusion/fusionscript.so"

   • Windows (Default):

     • RESOLVE_SCRIPT_API="%PROGRAMDATA%\Blackmagic Design\DaVinci Resolve\Support\Developer\Scripting"

     • RESOLVE_SCRIPT_LIB="C:\Program Files\Blackmagic Design\DaVinci Resolve\fusionscript.dll"

   • Linux (Default):

     • RESOLVE_SCRIPT_API="/opt/resolve/Developer/Scripting"

     • RESOLVE_SCRIPT_LIB="/opt/resolve/libs/Fusion/fusionscript.so"

⚙️ MCP Client Configuration

Add resolve-mcp-server to your MCP client configuration file (for example, claude_desktop_config.json or your IDE's MCP settings):

{
  "mcpServers": {
    "davinci-resolve": {
      "command": "/absolute/path/to/resolve-mcp-server/.venv/bin/resolve-mcp-server",
      "args": []
    }
  }
}

Or run the server directly during development:

python -m resolve_mcp_server

🛠️ Available MCP Tools

🧪 Running Tests

Unit tests use a mocked DaVinci Resolve API so you can test all MCP tools without launching DaVinci Resolve Studio:

python3 -m unittest discover -s tests

📂 Project Structure

resolve-mcp-server/
├── .github/
│   └── workflows/
│       └── ci.yml
├── .gitignore
├── LICENSE
├── README.md
├── pyproject.toml
├── src/
│   └── resolve_mcp_server/
│       ├── __init__.py
│       ├── __main__.py
│       ├── resolve_client.py
│       └── server.py
└── tests/
    └── test_server.py

🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the Project

2. Create your Feature Branch (git checkout -b feature/amazing-feature)

3. Commit your Changes (git commit -m 'Add amazing feature')

4. Push to the Branch (git push origin feature/amazing-feature)

5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.