Video Editor MCP Server

What does “Video Editor” MCP Server do?

The Video Editor MCP Server is a tool that bridges AI assistants with the Video Jungle video platform, enabling seamless integration for video upload, editing, searching, and generation tasks. By connecting AI workflows with Video Jungle’s APIs and resources, developers and users can automate video project creation, manage assets, generate edits based on context or search results, and interact with video content at scale. This server empowers large language models to perform complex video-related operations, such as analyzing video/audio content, live editing, and retrieving project metadata, all while leveraging the power of cloud and local resources. To use it, users must sign up for a Video Jungle account and provide their API key.

List of Prompts

No prompt templates are currently documented in the repository.

List of Resources

• Custom vj:// URI scheme
  Provides unique URIs for individual videos and projects, enabling direct referencing and access.
• Project Resources
  Each project resource is described with a name and description, helping organize generative scripts, analyzed videos, and images.
• Search Results Metadata
  Search queries return metadata about what’s in the video and when, facilitating edit generation and deeper content understanding.

List of Tools

• add-video
  Adds a video file for analysis from a URL and returns a vj:// URI for reference.
• create-videojungle-project
  Creates a new Video Jungle project to organize scripts, videos, and images for editing.
• edit-locally
  Downloads an OpenTimelineIO project for local editing in Davinci Resolve Studio (requires Resolve Studio running).
• generate-edit-from-videos
  Generates a rendered video edit from multiple video files.
• generate-edit-from-single-video
  Creates an edit from a single input video file.
• get-project-assets
  Retrieves assets from a project for video edit generation.
• search-videos
  Finds video matches using embeddings and keywords.
• update-video-edit
  Updates a video edit’s information in real-time if Video Jungle is open.

Use Cases of this MCP Server

• Automated Video Upload and Analysis
  Use AI to upload video files from URLs, analyze them for audio and visual content, and make them searchable for later retrieval.
• Video Search and Metadata Retrieval
  Perform semantic or keyword-based searches across your video library, returning detailed metadata for content discovery and edit planning.
• Automated Video Edit Generation
  Generate new video edits programmatically from either a single video or multiple sources, based on context or search results.
• Local Video Project Editing
  Download projects as OpenTimelineIO files for advanced local editing in professional tools like Davinci Resolve Studio.
• Real-time Edit Updates
  Update video edit information live, ensuring that changes are reflected instantly in open Video Jungle sessions.

How to set it up

Windsurf

1. Ensure Node.js and Windsurf are installed.
2. Obtain your Video Jungle API key from Video Jungle settings .
3. Add the Video Editor MCP server configuration to your Windsurf config file.
4. Use the generic package name: @video-editor/mcp-server@latest.
5. Save your configuration and restart Windsurf.

Example JSON:

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

Securing API Key:

{
  "mcpServers": {
    "video-editor-mcp": {
      "command": "video-editor-mcp",
      "env": {
        "VIDEO_JUNGLE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Claude

1. Install Claude Desktop.
2. Obtain your Video Jungle API key.
3. Edit claude_desktop_config.json (Mac: ~/Library/Application Support/Claude/claude_desktop_config.json, Windows: %APPDATA%/Claude/claude_desktop_config.json).
4. Add the Video Editor MCP server configuration.
5. Save and restart Claude Desktop.

Example JSON:

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

Securing API Key:

{
  "mcpServers": {
    "video-editor-mcp": {
      "command": "video-editor-mcp",
      "env": {
        "VIDEO_JUNGLE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cursor

1. Install Node.js and Cursor.
2. Obtain your Video Jungle API key.
3. Edit your Cursor configuration to include the MCP server.
4. Use the generic package name: @video-editor/mcp-server@latest.
5. Save and restart Cursor.

Example JSON:

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

Securing API Key:

{
  "mcpServers": {
    "video-editor-mcp": {
      "command": "video-editor-mcp",
      "env": {
        "VIDEO_JUNGLE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cline

1. Install Cline and Node.js.
2. Obtain your API key from Video Jungle.
3. Edit your Cline config to add the Video Editor MCP server.
4. Use the package: @video-editor/mcp-server@latest.
5. Save and restart Cline.

Example JSON:

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

Securing API Key:

{
  "mcpServers": {
    "video-editor-mcp": {
      "command": "video-editor-mcp",
      "env": {
        "VIDEO_JUNGLE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Note: Always use environment variables to keep your API keys secure.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "video-editor-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities.

Overview

I would rate the Video Editor MCP as a 7 out of 10: it offers robust tool and resource integration, secure setup, and supports several platforms. However, it lacks documented prompt templates and explicit support for advanced MCP features like roots and sampling.

MCP Score