Qhdrl12 MCP Server Gemini Image Generator

🚀 Using the Gemini Image Generator MCP Server

Leverage Google's Gemini model to generate high - quality images from text prompts via the MCP protocol.

🚀 Quick Start

This MCP server enables any AI assistant to generate images using Google's Gemini AI model. It handles prompt engineering, text - to - image conversion, intelligent filename generation, and local image storage, simplifying the creation and management of AI - generated images through any MCP client.

✨ Features

• Text - to - Image Generation: Generate high - quality images from text descriptions.
• Image Transformation: Edit and modify existing images based on prompts.
• Intelligent Filenames: Automatically generate meaningful filenames related to the generated images.
• Multi - format Support: Support multiple image output formats, such as PNG, JPEG, etc.

📚 Documentation

MCP Tools Used

1. generate_image_from_text

Generate a new image based on a text description.

2. transform_image

Edit an existing image based on a prompt.

3. apply_stylization

Apply a style to an existing image.

📦 Installation

Installation Steps

1. Clone the repository:

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

2. Create a virtual environment and install dependencies:

# Use regular venv
python -m venv .venv
source .venv/bin/activate
pip install -e .

# Or use uv
uv venv
source .venv/bin/activate
uv pip install -e .

3. Copy the example environment file and add the API key:

cp .env.example .env

4. Edit the .env file and enter the Gemini API key and the target output path:

GEMINI_API_KEY="your-gemini-api-key-here"
OUTPUT_IMAGE_PATH="/path/to/save/images"

Configure Claude Desktop

Add the following content to claude_desktop_config.json (taking macOS as an example):

{
    "mcpServers": {
        "gemini-image-generator": {
            "command": "uv",
            "args": [
                "--directory",
                "/absolute/path/to/gemini-image-generator",
                "run",
                "server.py"
            ],
            "env": {
                "GEMINI_API_KEY": "GEMINI_API_KEY",
                "OUTPUT_IMAGE_PATH": "OUTPUT_IMAGE_PATH"
            }
        }
    }
}

💻 Usage Examples

Basic Usage

After installation, you can start the service with the following command:

fastmcp dev server.py

This will start a local development server and provide an MCP inspection interface at http://localhost:5173/, allowing users to directly test the image generation tools.

📄 License

MIT License