Gemini Media Analysis

🚀 MCP Video Recognition Server

A server based on the Model Context Protocol (MCP) that provides image, audio, and video recognition tools 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

1. Clone the repository:

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

2. Install dependencies:

   npm install
   

3. Build the project:

   npm run build
   

Installation in FLUJO

1. Click "Add Server".
2. Copy and paste the Github URL into FLUJO.
3. Click "Resolve", "Clone", "Install", "Build", and "Save".

Installation via Configuration File

To integrate with Cline or other MCP clients via a configuration file:

1. Open your Cline settings:

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

2. 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": []
       }
     }
   }
   

3. Replace /path/to/mcp-video-recognition/dist/index.js with the actual path to the index.js file in the project directory. Use forward slashes (/) or double backslashes (\) on Windows.

4. Save the settings file. Cline should automatically connect to the server.

⚙️ Configuration

Configure the server using the following environment variables:

• GOOGLE_API_KEY: Google Gemini API key.
• TRANSPORT_TYPE: Transport type (e.g., http, grpc).
• PORT: Server port.

💻 Usage Examples

Starting the Server

npm start

Tool Usage Instructions

Basic Usage

Image Recognition

{
  "tool": "image_recognition",
  "params": {
    "input_path": "path/to/image.jpg"
  }
}

Audio Recognition

{
  "tool": "audio_recognition",
  // Assume there is more content here in the original, keep it as is
}