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).
To install automatically via Smithery:
npx -y @smithery/cli install @qhdrl12/mcp-server-gemini-image-gen --client claude
git clone https://github.com/your-username/gemini-image-generator.git
cd gemini-image-generator
# 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 .
cp .env.example .env
.env
file:GEMINI_API_KEY="your-gemini-api-key-here"
OUTPUT_IMAGE_PATH="/path/to/save/images"
Add the following to your claude_desktop_config.json
:
~/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"
}
}
}
}
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 generateReturns:
Examples:
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 imageReturns:
Examples:
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 transformedprompt
: Text description of how you want to transform the imageReturns:
Examples:
Once installed and configured, you can ask your AI assistant to generate or transform images with prompts like:
When using this MCP server with Claude Desktop Host:
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.
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.
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.
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.
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"
]
}
}
}
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.
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.