video-editing-mcp
- Image & Video Processing
Upload, edit, and generate videos from everyone's favorite LLM and Video Jungle.
Prompts
Interactive templates invoked by user choice
Name | Description |
---|---|
No prompts |
Resources
Contextual data attached and managed by the client
Name | Description |
---|---|
No resources |
Tools
Functions exposed to the LLM to take actions
Name | Description |
---|---|
No tools |
Server Configuration
Describes the environment variables required to run the server.
Name | Required | Description | Default |
---|---|---|---|
YOURAPIKEY | Yes | Your Video Jungle API key | |
UV_PUBLISH_TOKEN | No | Token for publishing to PyPI | |
UV_PUBLISH_PASSWORD | No | Password for publishing to PyPI | |
UV_PUBLISH_USERNAME | No | Username for publishing to PyPI |
Video Editor MCP server
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.
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 from a URL
- Returns an vj:// URI to reference the Video file
- search-videos: Search videos using embeddings
- Returns video matches based upon embeddings and keywords
- 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
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:
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:
Search results contain relevant metadata for generating a video edit according to details discovered in the initial analysis.
generate-edit-from-videos
Finally, you can use these search results to generate an edit:
(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:
Configuration
You must login to Video Jungle settings, and get your API key. Then, use this to start Video Jungle MCP:
Quickstart
Install
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
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:
- Sync dependencies and update lockfile:
- Build package distributions:
This will create source and wheel distributions in the dist/
directory.
- Publish to PyPI:
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--token
orUV_PUBLISH_TOKEN
- Or username/password:
--username
/UV_PUBLISH_USERNAME
and--password
/UV_PUBLISH_PASSWORD
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.)
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:
A reasonable way to follow along as you're workin on the project is to open a terminal session and do a:
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues with dependencies of the server.
- Extract server characteristics such as tools, resources, prompts, and required parameters.
Our directory badge helps users to quickly asses that the MCP server is safe, server capabilities, and instructions for installing the server.
Copy the following code to your README.md file: