Gemini Image Generator MCP server

This MCP server enables AI assistants to generate high-quality images using Google's Gemini model. Through the Model Context Protocol (MCP), it handles text-to-image conversion, image transformation, and provides both the image data and file storage path for seamless integration with AI assistants.

Prerequisites

Before installing the Gemini Image Generator MCP server, you'll need:

• Python 3.11 or higher
• 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

Installation Options

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/mcp-server-gemini-image-generator.git
cd mcp-server-gemini-image-generator

2. Create a virtual environment and install dependencies:

# Using uv (recommended)
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

# Or using regular venv
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

3. Set up environment variables (choose one method):

Method A: Using .env file (optional)

# Create .env file in the project root
cat > .env << 'EOF'
GEMINI_API_KEY=your-gemini-api-key-here
OUTPUT_IMAGE_PATH=/path/to/save/images
EOF

Method B: Set directly in Claude Desktop config (recommended)

• Set environment variables in the claude_desktop_config.json as shown below

Configuring Claude Desktop

Add the following to your claude_desktop_config.json:

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

{
    "mcpServers": {
        "gemini-image-generator": {
            "command": "uv",
            "args": [
                "--directory",
                "/absolute/path/to/mcp-server-gemini-image-generator",
                "run",
                "mcp-server-gemini-image-generator"
            ],
            "env": {
                "GEMINI_API_KEY": "your-actual-gemini-api-key-here",
                "OUTPUT_IMAGE_PATH": "/absolute/path/to/your/images/directory"
            }
        }
    }
}

Important Configuration Notes:

• Replace all paths with your actual absolute paths
• Use your real Gemini API key from Google AI Studio
• Example with real paths:

{
    "mcpServers": {
        "gemini-image-generator": {
            "command": "uv",
            "args": [
                "--directory",
                "/Users/username/Projects/mcp-server-gemini-image-generator",
                "run",
                "mcp-server-gemini-image-generator"
            ],
            "env": {
                "GEMINI_API_KEY": "GEMINI_API_KEY",
                "OUTPUT_IMAGE_PATH": "OUTPUT_IMAGE_PATH"
            }
        }
    }
}

Using the MCP Tools

The server provides three main tools for generating and transforming images:

Generate Image From Text

Creates a new image from a text prompt description:

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

Parameters:

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

Returns:

• A tuple containing:
  • Raw image data (bytes)
  • Path to the saved image file (str)

Example prompts:

• "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
• prompt: Text description of how you want to transform the image

Example prompts:

• "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

Example prompts:

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

Practical Usage Examples

Once installed and configured, you can ask Claude to generate or transform images using natural language prompts:

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"

The generated/transformed images will be saved to your configured output path and displayed in Claude. The dual return format allows AI assistants to either work with the image data directly or reference the saved file path.

Testing the Server

You can test the application using the FastMCP development server:

fastmcp dev server.py

This starts a local development server with the MCP Inspector available at http://localhost:5173/. The web interface lets you test the image generation tools directly without needing Claude or another MCP client.

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "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"}}'

See the official Claude Code MCP documentation for more details.

For 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 > Tools & Integrations and click "New MCP Server".

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

{
    "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"
            }
        }
    }
}

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 explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

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

2. Add this to your configuration file:

{
    "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"
            }
        }
    }
}

3. Restart Claude Desktop for the changes to take effect