MCP Video Recognition Server

An MCP (Model Context Protocol) server that provides tools for image, audio, and video recognition using Google's Gemini AI.

Features

Image Recognition: Analyze and describe images using Google Gemini AI
Audio Recognition: Analyze and transcribe audio using Google Gemini AI
Video Recognition: Analyze and describe videos using Google Gemini AI

Prerequisites

Node.js 18 or higher
Google Gemini API key

Installation

Manual Installation

Clone the repository:

git clone https://github.com/yourusername/mcp-video-recognition.git
cd mcp-video-recognition

Install dependencies:
```
npm install
```
Build the project:
```
npm run build
```

Installing in FLUJO

Click Add Server
Copy & Paste Github URL into FLUJO
Click Parse, Clone, Install, Build and Save.

Installing via Configuration Files

To integrate this MCP server with Cline or other MCP clients via configuration files:

Open your Cline settings:
- In VS Code, go to File -> Preferences -> Settings
- Search for "Cline MCP Settings"
- Click "Edit in settings.json"

Add the server configuration to the mcpServers object:

{
  "mcpServers": {
    "video-recognition": {
      "command": "node",
      "args": [
        "/path/to/mcp-video-recognition/dist/index.js"
      ],
      "disabled": false,
      "autoApprove": []
    }
  }
}

Replace /path/to/mcp-video-recognition/dist/index.js with the actual path to the index.js file in your project directory. Use forward slashes (/) or double backslashes (\\) for the path on Windows.
Save the settings file. Cline should automatically connect to the server.

Configuration

The server is configured using environment variables:

GOOGLE_API_KEY (required): Your Google Gemini API key
TRANSPORT_TYPE: Transport type to use (stdio or sse, defaults to stdio)
PORT: Port number for SSE transport (defaults to 3000)
LOG_LEVEL: Logging level (verbose, debug, info, warn, error, defaults to info)

Usage

Starting the Server

With stdio Transport (Default)

GOOGLE_API_KEY=your_api_key npm start

With SSE Transport

GOOGLE_API_KEY=your_api_key TRANSPORT_TYPE=sse PORT=3000 npm start

Using the Tools

The server provides three tools that can be called by MCP clients:

Image Recognition

{
  "name": "image_recognition",
  "arguments": {
    "filepath": "/path/to/image.jpg",
    "prompt": "Describe this image in detail",
    "modelname": "gemini-2.0-flash"
  }
}

Audio Recognition

{
  "name": "audio_recognition",
  "arguments": {
    "filepath": "/path/to/audio.mp3",
    "prompt": "Transcribe this audio",
    "modelname": "gemini-2.0-flash"
  }
}

Video Recognition

{
  "name": "video_recognition",
  "arguments": {
    "filepath": "/path/to/video.mp4",
    "prompt": "Describe what happens in this video",
    "modelname": "gemini-2.0-flash"
  }
}

Tool Parameters

All tools accept the following parameters:

filepath (required): Path to the media file to analyze
prompt (optional): Custom prompt for the recognition (defaults to "Describe this content")
modelname (optional): Gemini model to use for recognition (defaults to "gemini-2.0-flash")

Development

Running in Development Mode

GOOGLE_API_KEY=your_api_key npm run dev

Project Structure

src/index.ts: Entry point
src/server.ts: MCP server implementation
src/tools/: Tool implementations
src/services/: Service implementations (Gemini API)
src/types/: Type definitions
src/utils/: Utility functions

License

MIT

Gemini Media Analysis