MCP SERVERGenerate and edit images with Gemini from a chat — text to image, or an existing image transformed by a prompt, saved to a folder you choose.
LABSCO SUMMARY
Transformation from base64 data moves the whole image through the protocol and is documented as noticeably slower, and on Claude Desktop the returned file paths may not resolve for the host. Pass a file path to transform_image_from_file where you can, and set OUTPUT_IMAGE_PATH to a directory you can open yourself so a path that the client cannot follow is still a file you can find.
An MCP server that puts Google's Gemini 2.0 Flash image generation behind three tools. It handles the parts around the model call: naming the file from the prompt, translating non-English prompts, saving to a configured directory, and returning both the raw image data and the saved path so a client can use whichever it can handle.
generate_image_from_texttransform_image_from_filetransform_image_from_encodedA Google AI API key for Gemini, from AI Studio, in GEMINI_API_KEY; the key comes with a monthly free quota you can check in AI Studio. Set OUTPUT_IMAGE_PATH to an absolute directory — images are written there and the path is what gets returned. Python 3.11+, installed from a clone with uv pip install -e . and launched with uv run.
Build from source — git clone https://github.com/qhdrl12/mcp-server-gemini-image-generator.git
USE CASES
Describe the image you want and get it back inline plus written to your output folder, named after the prompt so it can be found later.
Point at the file on disk and say what should change — weather, time of day, something added to the scene — and get a new file rather than an overwrite.
Generate a first version, then feed the saved file back in with a follow-up prompt, repeating until it is right.
Send base64 data with a format header when there is no file on disk to point at, accepting that this path moves more data through the protocol.
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
generate_image_from_textGenerate an image based on the given text prompt using Google's Gemini model.
Args: prompt: User's text prompt describing the desired image to generate model: The Gemini model to use (default: gemini-3-pro-image-preview)
Returns: Path to the generated image file using Gemini's image generation capabilities
transform_image_from_encodedTransform an existing image based on the given text prompt using Google's Gemini model.
Args: encoded_image: Base64 encoded image data with header. Must be in format: "data:image/[format];base64,[data]" Where [format] can be: png, jpeg, jpg, gif, webp, etc. prompt: Text prompt describing the desired transformation or modifications model: The Gemini model to use (default: gemini-3-pro-image-preview)
Returns: Path to the transformed image file saved on the server
transform_image_from_fileTransform an existing image file based on the given text prompt using Google's Gemini model.
Args: image_file_path: Path to the image file to be transformed prompt: Text prompt describing the desired transformation or modifications model: The Gemini model to use (default: gemini-3-pro-image-preview)
Returns: Path to the transformed image file saved on the server
BEFORE YOU INSTALL
3 tools when we connected.
The documented client runs the server on your machine.
CONTEXT COST
Rated by the size of the 3 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
You clone this one yourself.
git clone https://github.com/qhdrl12/mcp-server-gemini-image-generator.git
cd mcp-server-gemini-image-generator
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -e .{
"mcpServers": {
"gemini-image-generator": {
"command": "uv",
"args": [
"--directory",
"<YOUR_PATH_TO_MCP_SERVER_GEMINI_IMAGE>",
"run",
"mcp-server-gemini-image-generator"
],
"env": {
"GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>",
"OUTPUT_IMAGE_PATH": "<YOUR_OUTPUT_IMAGE_PATH>"
}
}
}
}MCP servers
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