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Video Editor

Community
burningion

MCP Interface for Video Jungle

Publisherburningion
Repositoryvideo-editing-mcp
LanguagePython
Forks
38
Stars
267
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Video Editor exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    267 stars and 38 forks from the linked repository.

Video Editor MCP server

Video Jungle MCP Server

See a demo here: https://www.youtube.com/watch?v=KG6TMLD8GmA

Upload, edit, search, and generate videos from everyone's favorite LLM and Video Jungle.

You'll need to sign up for an account at Video Jungle in order to use this tool, and add your API key.

PyPI version

Components

Resources

The server implements an interface to upload, generate, and edit videos with:

  • Custom vj:// URI scheme for accessing individual videos and projects
  • Each project resource has a name, description
  • Search results are returned with metadata about what is in the video, and when, allowing for edit generation directly

Prompts

Coming soon.

Tools

The server implements a few tools:

  • add-video
    • Add a Video File for analysis from a URL. Returns an vj:// URI to reference the Video file
  • create-videojungle-project
    • Creates a Video Jungle project to contain generative scripts, analyzed videos, and images for video edit generation
  • edit-locally
    • Creates an OpenTimelineIO project and downloads it to your machine to open in a Davinci Resolve Studio instance (Resolve Studio must already be running before calling this tool.)
  • generate-edit-from-videos
    • Generates a rendered video edit from a set of video files
  • generate-edit-from-single-video
    • Generate an edit from a single input video file
  • get-project-assets
    • Get assets within a project for video edit generation.
  • search-videos
    • Returns video matches based upon embeddings and keywords
  • update-video-edit
    • Live update a video edit's information. If Video Jungle is open, edit will be updated in real time.

Using Tools in Practice

In order to use the tools, you'll need to sign up for Video Jungle and add your API key.

add-video

Here's an example prompt to invoke the add-video tool:

can you download the video at https://www.youtube.com/shorts/RumgYaH5XYw and name it fly traps?

This will download a video from a URL, add it to your library, and analyze it for retrieval later. Analysis is multi-modal, so both audio and visual components can be queried against.

search-videos

Once you've got a video downloaded and analyzed, you can then do queries on it using the search-videos tool:

can you search my videos for fly traps?

Search results contain relevant metadata for generating a video edit according to details discovered in the initial analysis.

search-local-videos

You must set the environment variable LOAD_PHOTOS_DB=1 in order to use this tool, as it will make Claude prompt to access your files on your local machine.

Once that's done, you can search through your Photos app for videos that exist on your phone, using Apple's tags.

In my case, when I search for "Skateboard", I get 1903 video files.

can you search my local video files for Skateboard?

generate-edit-from-videos

Finally, you can use these search results to generate an edit:

can you create an edit of all the times the video says "fly trap"?

(Currently), the video edits tool relies on the context within the current chat.

generate-edit-from-single-video

Finally, you can cut down an edit from a single, existing video:

can you create an edit of all the times this video says the word "fly trap"?

Configuration

You must login to Video Jungle settings, and get your API key. Then, use this to start Video Jungle MCP:

bash
$ uv run video-editor-mcp YOURAPIKEY

To allow this MCP server to search your Photos app on MacOS:

$ LOAD_PHOTOS_DB=1 uv run video-editor-mcp YOURAPIKEY

Quickstart

Install

Installing via Smithery

To install Video Editor for Claude Desktop automatically via Smithery:

bash
npx -y @smithery/cli install video-editor-mcp --client claude

Claude Desktop

You'll need to adjust your claude_desktop_config.json manually:

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

json
 "mcpServers": {
   "video-editor-mcp": {
     "command": "uvx",
     "args": [
       "video-editor-mcp",
       "YOURAPIKEY"
     ]
   }
 }
json
 "mcpServers": {
   "video-editor-mcp": {
     "command": "uv",
     "args": [
       "--directory",
       "/Users/YOURDIRECTORY/video-editor-mcp",
       "run",
       "video-editor-mcp",
       "YOURAPIKEY"
     ]
   }
 }

With local Photos app access enabled (search your Photos app):

json
  "video-jungle-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/Users/<PATH_TO>/video-jungle-mcp",
      "run",
      "video-editor-mcp",
      "<YOURAPIKEY>"
    ],
   "env": {
        "LOAD_PHOTOS_DB": "1"
    }
  },

Be sure to replace the directories with the directories you've placed the repository in on your computer.

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
bash
uv sync
  1. Build package distributions:
bash
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
bash
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

MCP Server Registry

mcp-name: io.github.burningion/video-editing-mcp

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

(Be sure to replace YOURDIRECTORY and YOURAPIKEY with the directory this repo is in, and your Video Jungle API key, found in the settings page.)

bash
npx @modelcontextprotocol/inspector uv run --directory /Users/YOURDIRECTORY/video-editor-mcp video-editor-mcp YOURAPIKEY

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Additionally, I've added logging to app.log in the project directory. You can add logging to diagnose API calls via a:

logging.info("this is a test log")

A reasonable way to follow along as you're workin on the project is to open a terminal session and do a:

bash
$ tail -n 90 -f app.log

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "video-editor-mcp": {
      "command": "uvx",
      "args": [
        "video-editor-mcp"
      ]
    }
  }
}

Use Video Editor MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Video Editor is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Video Editor as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Video Editor MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "video-editor": {
      "command": "npx",
      "args": [
        "-y",
        "video-editor-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Video Editor MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Video Editor MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Video Editor to help me with this task?
Video Editor
Sure. I read it.
Here is what I found using Video Editor.

Frequently asked questions

What is the Video Editor MCP server used for?

Video Editor is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Video Editor MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Video Editor with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Video Editor MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Video Editor connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Video Editor MCP to TypingMind?

Video Editor runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Video Editor MCP provide in TypingMind?

Video Editor exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Video Editor MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Video Editor requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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