DaVinci Resolve MCP Server

The DaVinci Resolve MCP Server bridges the gap between powerful video‑editing workflows and conversational AI assistants such as Cursor or Claude Desktop. By exposing DaVinci Resolve’s internal API through the Model Context Protocol, developers can ask natural‑language queries—“Show me the current timeline length”, “Add a color grade to clip 3”—and receive real‑time responses or execute editing commands without leaving the assistant’s interface. This eliminates the need for manual scripting or screen‑scraping, streamlining production pipelines and reducing context switching.

At its core, the server listens for MCP requests on a local port, translates them into DaVinci Resolve SDK calls, and returns structured JSON responses. It supports a wide range of operations: querying project metadata, manipulating timelines, inserting or removing clips, adjusting color grades, and even initiating render jobs. Because the server runs alongside Resolve in the background, it can monitor state changes and push updates to the assistant, enabling proactive notifications such as “Render completed” or “Clip 5 has a new watermark.” This bidirectional flow turns the assistant into an intelligent co‑operator rather than a passive tool.

Key capabilities include:

• Cross‑platform compatibility (macOS and Windows) with automated installation scripts that detect Resolve paths, set up virtual environments, and configure environment variables.
• Extensible feature set documented in , covering both current commands and planned enhancements like multi‑track editing or batch operations.
• Robust error handling that logs issues to , simplifying debugging for developers.
• Seamless integration with existing MCP clients; configuration templates allow quick adaptation to different assistant platforms.

Real‑world scenarios that benefit from this server are abundant. A post‑production team can use the assistant to generate quick previews, apply consistent color grading presets across multiple clips, or automate repetitive tasks such as adding captions. Freelancers and small studios can prototype edits in a conversational manner, speeding up the feedback loop with clients. Moreover, educators teaching video editing can harness natural‑language commands to demonstrate concepts without needing to expose the full Resolve interface.

What sets this MCP server apart is its focus on developer ergonomics. The installation process is intentionally lightweight—just a single script that handles environment setup, path detection, and optional client configuration. The server’s API is intentionally high‑level yet expressive enough to cover complex editing workflows, making it a practical addition to any AI‑augmented media pipeline.