Gemini Image Generator MCP server

This MCP server allows you to generate high-quality images from text descriptions using Google's Gemini AI model. It provides a simple way for AI assistants to create and transform images through the Model Context Protocol (MCP).

Installation

Prerequisites

• Python 3.11+
• Google AI API key (Gemini)
• MCP host application (Claude Desktop App, Cursor, or other MCP-compatible clients)

Getting a Gemini API Key

1. Visit Google AI Studio API Keys page
2. Sign in with your Google account
3. Click "Create API Key"
4. Copy your new API key for use in the configuration
5. Note: The API key provides a certain quota of free usage per month

Installing via Smithery

To install automatically via Smithery:

npx -y @smithery/cli install @qhdrl12/mcp-server-gemini-image-gen --client claude

Manual Installation

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:

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

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

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

cp .env.example .env

4. Edit the .env file:

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

Configure Claude Desktop

Add the following to your claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
    "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"
            }
        }
    }
}

Features

• Text-to-image generation using Gemini 2.0 Flash
• Image-to-image transformation based on text prompts
• Support for both file-based and base64-encoded images
• Automatic intelligent filename generation
• Automatic translation of non-English prompts
• Local image storage with configurable output path
• High-resolution image output

Available Tools

Generate Image From Text

Creates a new image from a text description.

generate_image_from_text(prompt: str) -> Tuple[bytes, str]

Parameters:

• prompt: Text description of the image you want to generate

Returns:

• Raw image data (bytes)
• Path to the saved image file (str)

Examples:

• "Generate an image of a sunset over mountains"
• "Create a photorealistic flying pig in a sci-fi city"

Transform Image From Encoded

Transforms an existing image based on a text prompt using base64-encoded image data.

transform_image_from_encoded(encoded_image: str, prompt: str) -> Tuple[bytes, str]

Parameters:

• encoded_image: Base64 encoded image data with format header (must be in format: "data:image/[format];base64,[data]")
• prompt: Text description of how you want to transform the image

Returns:

• Raw transformed image data (bytes)
• Path to the saved transformed image file (str)

Examples:

• "Add snow to this landscape"
• "Change the background to a beach"

Transform Image From File

Transforms an existing image file based on a text prompt.

transform_image_from_file(image_file_path: str, prompt: str) -> Tuple[bytes, str]

Parameters:

• image_file_path: Path to the image file to be transformed
• prompt: Text description of how you want to transform the image

Returns:

• Raw transformed image data (bytes)
• Path to the saved transformed image file (str)

Examples:

• "Add a llama next to the person in this image"
• "Make this daytime scene look like night time"

Usage Examples

Once installed and configured, you can ask your AI assistant to generate or transform images with prompts like:

Generating New Images

• "Generate an image of a sunset over mountains"
• "Create an illustration of a futuristic cityscape"
• "Make a picture of a cat wearing sunglasses"

Transforming Existing Images

• "Transform this image by adding snow to the scene"
• "Edit this photo to make it look like it was taken at night"
• "Add a dragon flying in the background of this picture"

Known Issues

When using this MCP server with Claude Desktop Host:

1. Performance Issues: Using transform_image_from_encoded may take significantly longer to process compared to other methods due to the overhead of transferring large base64-encoded image data.

2. Path Resolution Problems: There may be issues with correctly resolving image paths when using Claude Desktop Host.

For the best experience, consider using alternative MCP clients or the transform_image_from_file method when possible.

Testing

You can test the application by running the FastMCP development server:

fastmcp dev server.py

This starts a local development server with the MCP Inspector available at http://localhost:5173/, where you can test the image generation tools directly.

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.