# davinci-resolve-lite-mcp

> davinci-resolve-lite-mcp — 2sem-davinci-resolve-lite-mcp. Use this tool when you need to automate and control DaVinci Resolve, including the free Lite edition, for video editing, color grading, and rendering tasks. It solves problems of manual editing and scripting by exposing 163 tools through a local HTTP server, allowing AI clients to interface with Resolve. Ideal for use cases requiring automated video post-production, media management, and title styling, with inputs including script commands and outputs including rendered video files.

Canonical page: https://skillsregistry.net/skills/2sem-davinci-resolve-lite-mcp  
JSON: https://api.skillsregistry.net/v1/skills/2sem-davinci-resolve-lite-mcp

## Description

Enables AI clients such as Claude Code to control DaVinci Resolve, including the free Lite edition, through a local HTTP server that runs inside Resolve's Scripts menu. It exposes 163 tools for editing, color, rendering, media pool, and Fusion title styling.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-09-04

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-09-04

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/tef22incyr)
- **Repository:** <https://github.com/2sem/davinci-resolve-lite-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "2sem-davinci-resolve-lite-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/2sem-davinci-resolve-lite-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/2sem-davinci-resolve-lite-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt