MCP Gemini Video Understanding

An MCP (Model Context Protocol) server that uses Google's Gemini API to analyze videos and convert them to text descriptions that Claude Code can understand and act upon.

What is this?

This MCP server acts as a bridge between video content and Claude Code. When you have a video (screen recording, Loom video, YouTube tutorial, etc.), this server uses Gemini's powerful video understanding capabilities to extract meaningful text descriptions that Claude Code can then use to write code, fix bugs, or implement features.

Related MCP server: Gemini Code Assist MCP

Use Cases

1. Bug Reproduction Videos: Record a video showing a bug → Get detailed steps to reproduce and debugging insights

2. Design Mockups: Show a design in a video → Get implementation guidance with UI component breakdowns

3. YouTube Tutorials: Share a tutorial URL → Extract key learnings and implementation steps

4. Responsive Issues: Record layout problems → Get specific CSS fixes and responsive solutions

Installation

npm install -g @ugarchance/mcp-gemini-video-understanding

Or use directly with npx:

npx @ugarchance/mcp-gemini-video-understanding

Setup

1. Get a Gemini API Key

1. Go to Google AI Studio

2. Click "Get API Key"

3. Create or select a project

4. Copy your API key

2. Set Environment Variable

export GEMINI_API_KEY="your-api-key-here"

3. Configure Claude Code

Add to your claude_desktop_config.json:

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

{
  "mcpServers": {
    "gemini-video": {
      "command": "npx",
      "args": [
        "-y",
        "@ugarchance/mcp-gemini-video-understanding"
      ],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Or if installed globally:

{
  "mcpServers": {
    "gemini-video": {
      "command": "mcp-gemini-video",
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Usage

All tools support these common parameters:

• model (string, optional): Gemini model to use. Options:

  • gemini-2.5-pro - Most capable, best for complex analysis

  • gemini-2.5-flash - Default, balanced speed and quality

  • gemini-2.5-flash-lite - Fastest, lighter analysis

  • gemini-2.0-flash - Previous generation fast model

  • gemini-2.0-flash-exp - Experimental features

• output_file (string, optional): Path to save analysis. If file exists, cached result is used (no re-analysis!)

Tool 1: analyze_bug_video

Analyze a video showing a bug or error.

Parameters:

• video_path (string): Path to video file or YouTube URL

• is_youtube (boolean, optional): Set to true if using YouTube URL

• additional_context (string, optional): Extra context about the bug

• model (string, optional): Gemini model to use

• output_file (string, optional): Path to save analysis

Example with Claude Code:

I have a bug video at /Users/me/Desktop/bug-demo.mp4
Save the analysis to bug-analysis.md and fix the issue.

With model selection:

Analyze /Users/me/Desktop/complex-bug.mp4 using gemini-2.5-pro
Save to analysis.txt and help me fix it.

Tool 2: analyze_design_video

Analyze a video showing a design mockup or feature demonstration.

Parameters:

• video_path (string): Path to video file or YouTube URL

• is_youtube (boolean, optional): Set to true if using YouTube URL

• tech_stack (string, optional): Technologies to use (e.g., "React with Tailwind")

• model (string, optional): Gemini model to use

• output_file (string, optional): Path to save analysis

Example with Claude Code:

I recorded a design mockup at /Users/me/Desktop/new-feature.mp4
Save analysis to design-spec.md then implement using React and Tailwind CSS.

Tool 3: analyze_tutorial_video

Analyze a YouTube tutorial to extract key learnings.

Parameters:

• video_url (string): YouTube URL

• focus_area (string, optional): Specific topic to focus on

• model (string, optional): Gemini model to use

• output_file (string, optional): Path to save analysis

Example with Claude Code:

Watch this tutorial: https://www.youtube.com/watch?v=xxxxx
Save the learnings to tutorial-notes.md then implement the auth system.

Using faster model for quick summaries:

Analyze https://www.youtube.com/watch?v=xxxxx with gemini-2.5-flash-lite
Just give me the key points.

Tool 4: analyze_responsive_issues

Analyze a video showing responsive design problems.

Parameters:

• video_path (string): Path to video file or YouTube URL

• is_youtube (boolean, optional): Set to true if using YouTube URL

• target_devices (string, optional): Target devices (e.g., "mobile, tablet")

• model (string, optional): Gemini model to use

• output_file (string, optional): Path to save analysis

Example with Claude Code:

I recorded responsive issues at /Users/me/Desktop/mobile-issues.mp4
Save analysis to responsive-fixes.md and fix the layout for mobile.

How It Works

1. You record a video or find a YouTube URL

2. You ask Claude Code to analyze it via MCP (optionally specifying model and output file)

3. MCP Server checks if cached analysis exists (if output_file specified)

4. If no cache: Sends video to Gemini API with chosen model

5. Gemini analyzes video and returns detailed text description

6. MCP Server saves result to file (if output_file specified)

7. Claude Code receives the text and can now write/fix code based on it

Caching Strategy

When you specify an output_file:

• First run: Video is analyzed and result is saved to the file

• Subsequent runs: Cached file is read instantly (no API call, no cost!)

• To re-analyze: Delete the output file first

This is perfect for:

• Iterating on implementations without re-analyzing videos

• Sharing analysis results with team members

• Reducing API costs and latency

Supported Video Formats

• MP4

• MOV

• AVI

• WebM

• MKV

• FLV

• WMV

• 3GP

• MPEG

Available Models

Limitations

• YouTube: Only public videos (not private or unlisted)

• File Size: Files >20MB automatically use Gemini's File API (may take longer to process)

• Video Length: Longer videos take more time to process

• Rate Limits: Subject to Gemini API rate limits

• Caching: Only works when output_file is specified

Development

Local Development

# Clone the repo
git clone https://github.com/ugarchance/mcp-gemini-video-understanding
cd mcp-gemini-video-understanding

# Install dependencies
npm install

# Build
npm run build

# Test locally with Claude Code
# Add to claude_desktop_config.json:
{
  "mcpServers": {
    "gemini-video": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-gemini-video-understanding/build/index.js"],
      "env": {
        "GEMINI_API_KEY": "your-key"
      }
    }
  }
}

Publishing to npm

# Update package.json with your npm username
npm login
npm publish

Troubleshooting

"GEMINI_API_KEY environment variable is required"

Make sure you've set the GEMINI_API_KEY in your claude_desktop_config.json under the env section.

"Error analyzing video"

• Check that the video file path is absolute (not relative)

• Verify the video format is supported

• For YouTube videos, ensure the URL is valid and the video is public

• Check Gemini API quotas and rate limits

Tools not showing in Claude Code

1. Restart Claude Code completely (Cmd+Q on Mac, not just close window)

2. Check claude_desktop_config.json syntax is valid JSON

3. Look at Claude Code logs: ~/Library/Logs/Claude/mcp*.log (macOS)

License

MIT

Contributing

Contributions welcome! Please open an issue or PR.

Credits

Built with:

• Gemini API for video understanding

• Model Context Protocol for Claude integration