Digital-Marketing-MCP-servers/mcp-server-gemini-image

MCP Server - Gemini Image Generator- AUTO UPDATED

#CRONTAB IS WORKING A Model Context Protocol (MCP) server that enables AI-driven image generation using Google's Gemini API. This server provides seamless integration with MCP-compatible clients for generating high-quality images from text prompts.

✨ Features

• 🖼️ Gemini-powered image generation with advanced AI models
• 🔌 MCP protocol compatibility for easy integration
• 🛠️ Configurable prompts and generation parameters
• 📁 Static file serving for generated images
• 🧪 Built-in testing and validation tools
• 🔒 Environment-based configuration for secure API key management

📂 Project Structure

mcp-server-gemini-image-generator/
├── .venv/                                    # Virtual environment
├── examples/                                 # Usage examples
├── src/mcp_server_gemini_image_generator/
│   ├── __init__.py                          # Package initialization
│   ├── server.py                            # Main MCP server
│   ├── prompts.py                           # Prompt management
│   ├── utils.py                             # Utility functions
│   └── test.py                              # Test suite
├── static/                                  # Generated images & assets
├── .env                                     # Environment configuration
├── .gitignore                               # Git ignore rules
├── pyproject.toml                           # Project metadata & dependencies
├── uv.lock                                  # Dependency lock file
├── smithery.yaml                            # Deployment configuration
├── LICENSE                                  # MIT License
└── README.md                                # This file

🚀 Quick Start

Prerequisites

• Python 3.8+
• Gemini API key (get one from Google AI Studio)

Installation

1. Clone the repository

   git clone https://github.com/your-username/mcp-server-gemini-image-generator.git
   cd mcp-server-gemini-image-generator
   

2. Set up virtual environment

   # Using Python venv
   python -m venv .venv
   source .venv/bin/activate  # Linux/macOS
   # .venv\Scripts\activate   # Windows
   
   # Or using uv (recommended)
   uv venv
   source .venv/bin/activate
   

3. Install dependencies

   # Using pip
   pip install -e .
   
   # Or using uv (faster)
   uv sync
   

4. Configure environment

   Create a .env file in the project root:

   API_KEY=your_gemini_api_key_here
   PORT=8002
   HOST=127.0.0.1
   

5. Start the server

   python src/mcp_server_gemini_image_generator/server.py
   

The MCP server will be available at: http://127.0.0.1:8002/mcp/

🔧 Configuration

Environment Variables

MCP Client Integration

Add this server to your MCP client configuration:

{
  "mcpServers": {
    "gemini-image-generator": {
      "command": "python",
      "args": ["path/to/src/mcp_server_gemini_image_generator/server.py"],
      "env": {
        "API_KEY": "your_gemini_api_key"
      }
    }
  }
}

🧪 Testing

Run the test suite to verify everything works:

python src/mcp_server_gemini_image_generator/test.py

📖 Usage Examples

Basic Image Generation

from mcp_server_gemini_image_generator import generate_image

# Generate an image from a text prompt
result = generate_image("A serene mountain landscape at sunset")
print(f"Generated image: {result['image_url']}")

Custom Prompts

# Use the prompt utilities for enhanced generation
from mcp_server_gemini_image_generator.prompts import enhance_prompt

enhanced = enhance_prompt("cat", style="photorealistic", mood="playful")
result = generate_image(enhanced)

🛠️ Development

Project Setup for Development

# Clone and setup
git clone https://github.com/your-username/mcp-server-gemini-image-generator.git
cd mcp-server-gemini-image-generator

# Install in development mode
uv sync --dev

# Run tests
python -m pytest src/mcp_server_gemini_image_generator/test.py

Adding New Features

1. Implement your feature in the appropriate module (server.py, prompts.py, or utils.py)
2. Add tests in test.py
3. Update documentation
4. Submit a pull request

📋 API Reference

MCP Tools

Response Format

{
  "success": true,
  "image_url": "http://127.0.0.1:8002/static/generated_image_123.png",
  "prompt_used": "Enhanced prompt that was actually used",
  "generation_time": 2.34,
  "metadata": {
    "model": "gemini-pro-vision",
    "timestamp": "2024-01-15T10:30:00Z"
  }
}

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