Gemini Image Generation MCP Server

A Model Context Protocol (MCP) server that provides image generation capabilities using Google's Gemini 2.0 Flash Preview model. This server allows AI assistants to generate high-quality images from text prompts through the MCP protocol.

Features

• Image Generation: Generate images from text prompts using Gemini 2.0 Flash Preview

• Multiple Output Formats: Support for PNG, JPEG, and other image formats

• File Management: Automatic saving of generated images with organized file naming

• Base64 Support: Handle image data in base64 format for easy integration

• Status Monitoring: Check API connection status and model information

• Prompt Templates: Pre-built prompts for optimized image generation

Related MCP server: Gemini Image MCP

Prerequisites

• Python 3.9 or higher

• Google AI API key (Gemini API access)

• uv package manager (recommended) or pip

Installation

Using uv (Recommended)

1. Clone or download this repository:

git clone <repository-url>
cd image-generation-gemini-mcp

2. Install dependencies:

uv sync

Using pip

1. Install dependencies:

pip install -r requirements.txt

Setup

1. Get Google AI API Key

1. Visit Google AI Studio

2. Create a new API key

3. Copy the API key for use in the next step

2. Set Environment Variable

Set your Gemini API key as an environment variable:

Windows (PowerShell):

$env:GEMINI_API_KEY="your-api-key-here"

Windows (Command Prompt):

set GEMINI_API_KEY=your-api-key-here

macOS/Linux:

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

For permanent setup, add the environment variable to your system's environment variables or shell profile.

Usage

Running the Server

Development Mode

To test the server locally:

uv run server.py

MCP Integration Mode

To run as an MCP server:

uv run server.py stdio

Integration with MCP Clients

Claude Desktop Integration

1. Add the server to your Claude Desktop configuration file:

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

{
  "mcpServers": {
    "gemini-image-generator": {
      "command": "uv",
      "args": ["run", "server.py", "stdio"],
      "cwd": "C:\\path\\to\\image-generation-gemini-mcp",
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

2. Restart Claude Desktop

3. The image generation tools will be available in your conversations

Other MCP Clients

For other MCP-compatible clients, configure them to run:

uv run server.py stdio

With the working directory set to this project folder and the GEMINI_API_KEY environment variable configured.

Available Tools

generate_image

Generate an image from a text prompt.

Parameters:

• prompt (string): Text description of the image to generate

• output_dir (string, optional): Directory to save the image (default: "./generated_images")

Returns:

• success (boolean): Whether generation was successful

• message (string): Status message or error description

• image_data (string): Base64 encoded image data (if successful)

• mime_type (string): MIME type of the generated image

• file_extension (string): File extension for the image

save_image_from_base64

Save a base64 encoded image to a file.

Parameters:

• image_data (string): Base64 encoded image data

• filename (string): Name for the output file (include extension)

• output_dir (string, optional): Directory to save the image

Returns:

• success (string): "true" or "false"

• message (string): Status message

• file_path (string): Path to saved file (if successful)

Available Resources

gemini://api-status

Check the status of the Gemini API connection.

gemini://model-info

Get information about the Gemini image generation model capabilities.

Available Prompts

image_generation_prompt

Generate a detailed prompt optimized for image generation.

Parameters:

• subject (string): Main subject or object to generate

• style (string, optional): Art style (default: "photorealistic")

• mood (string, optional): Mood or atmosphere (default: "neutral")

Example Usage

Once integrated with an MCP client like Claude Desktop, you can:

1. Generate an image:

   Please generate an image of a sunset over mountains

2. Use specific styles:

   Create a cartoon-style image of a friendly robot

3. Check API status:

   Can you check the status of the Gemini API?

4. Get model information:

   What are the capabilities of the image generation model?

File Structure

image-generation-gemini-mcp/
├── server.py              # Main MCP server implementation
├── requirements.txt       # Python dependencies
├── pyproject.toml        # Project configuration
├── README.md             # This file
├── docs/                 # Documentation files
│   ├── development-guidelines.md
│   ├── mcp-info.md
│   └── mcp-python-sdk-readme.md
└── generated_images/     # Default output directory (created automatically)

Troubleshooting

Common Issues

1. "GEMINI_API_KEY environment variable is required"

   • Ensure you've set the GEMINI_API_KEY environment variable

   • Verify the API key is valid and has access to Gemini API

2. "Error connecting to Gemini API"

   • Check your internet connection

   • Verify your API key is correct and active

   • Ensure you have access to the Gemini 2.0 Flash Preview model

3. "No image was generated"

   • Try rephrasing your prompt

   • Ensure your prompt is descriptive and clear

   • Check if there are any content policy restrictions

4. Permission errors when saving files

   • Ensure the output directory is writable

   • Check file system permissions

Getting Help

• Check the MCP documentation

• Review the Google AI documentation

• Ensure all dependencies are properly installed

Development

Code Quality

This project follows the MCP development guidelines:

• Formatting: uv run ruff format .

• Linting: uv run ruff check .

• Type checking: uv run pyright

Testing

Run tests with:

uv run pytest

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please follow the MCP development guidelines and ensure all code is properly formatted and type-checked before submitting.