samuelgursky/davinci-resolve-mcp
davinci-resolve-mcp is an open-source MCP (Model Context Protocol) server that bridges AI assistants with DaVinci Resolve, enabling natural language control of video editing, color grading, media management, and project control. It exposes a comprehensive set of tools and resources that allow AI agents to perform actions such as creating and managing projects, manipulating timelines and clips, applying color corrections, rendering deliverables, and managing media pools, all through a standardized interface. The server operates by connecting to DaVinci Resolve's Python scripting API, translating high-level MCP requests into Resolve API calls. It includes advanced capabilities like timeline version diffing, declarative project specifications (via YAML/JSON), and a task scheduler for automating repetitive workflows. With features like dry-run previews, idempotent operations, and dependency-ordered settings, it ensures safe and predictable automation. Designed for extensibility and ease of deployment, it can be run locally or in a server environment, and is compatible with any MCP-compliant AI client (e.g., Claude, GPT). It reduces the barrier to programmatic control of Resolve, empowering both technical and non-technical users to orchestrate complex post-production pipelines with conversational commands.
- Category
- Developer Tools
- Pricing
- Open Source
Problem solved
Video editors, colorists, and content creators often face repetitive manual tasks in DaVinci Resolve, such as setting up project structures, syncing timelines, applying consistent color grades, and rendering deliverables. Existing automation requires scripting knowledge and direct use of Resolve's API, which is inaccessible to many creative professionals. davinci-resolve-mcp solves this by providing an AI-friendly bridge that allows users to describe desired outcomes in plain English, and have an AI assistant execute the necessary Resolve operations automatically, saving time and reducing technical friction.
Use cases
- Automating the creation of a new DaVinci Resolve project with predefined settings, bins, and timeline structure via an AI chat interface.
- Batch-applying a specific color grade to multiple clips across different timelines using natural language instructions.
- Generating and applying declarative project specifications (`.dvr.yaml`) to enforce consistent project templates across a team.
- Comparing timeline versions to identify changes (added, removed, moved, trimmed clips) during collaborative editing.
- Scheduling recurring tasks such as nightly renders or media pool reorganization using the built-in task scheduler.
- Integrating DaVinci Resolve into automated media pipelines where AI agents orchestrate multi-step post-production workflows.
Audience
- Developers
- Content Creators
- Designers
- ML Engineers
Pros
- Enables natural language control of DaVinci Resolve via any MCP-compatible AI assistant.
- Comprehensive toolset covering projects, timelines, clips, color grading, rendering, and media management.
- Advanced features like declarative specs, timeline diff, and task scheduler enhance automation capabilities.
- Open source with active development and community support.
- Cross-platform compatibility (macOS, Windows, Linux) assuming Resolve Studio is available.
Cons
- Requires DaVinci Resolve Studio (paid version) to be installed and running for the scripting API connection.
- Setup complexity may be high for non-technical users, involving Python environment management and Resolve API configuration.
- Limited by the capabilities and stability of the official Resolve scripting API; some advanced features may be unavailable.
Official website: https://github.com/samuelgursky/davinci-resolve-mcp
Canonical LambdaBase page: https://www.lambdabase.com/tools/samuelgursky-davinci-resolve-mcp