Gemini Image Generator MCP Server: Pro Precision & Smart Filenames

How to Use Gemini Image Generator MCP Server: Pro Precision & Smart Filenames?

Follow this streamlined workflow:

1. Configure: Set API credentials and storage paths via environment variables
2. Deploy: Integrate with your MCP client using provided configuration templates
3. Create: Issue natural language prompts like "Generate a cyberpunk Tokyo scene with neon lights"
4. Edit: Modify existing images through commands like "Add snowfall effect to mountain landscape"
5. Retrieve: Access outputs immediately via your configured storage location

Use Cases of Gemini Image Generator MCP Server: Pro Precision & Smart Filenames?

Optimal for scenarios requiring:

• High-fidelity concept art for game development
• Dynamic social media visuals with automated naming
• Iterative A/B testing for marketing assets
• Architectural visualization with version control
• Content generation for e-commerce product showcases

Example prompt patterns:

"Create a photorealistic image of a vintage coffee shop interior with warm lighting"

FAQ from Gemini Image Generator MCP Server: Pro Precision & Smart Filenames?

How do I handle API rate limits?

Implement exponential backoff strategies using the built-in retry mechanisms

Can I customize filename formats?

Yes - modify the naming logic in server.py to include custom tags or separators

What image resolutions are supported?

Generates at 1024x1024 by default, adjustable through prompt parameters

How is image quality maintained during edits?

Uses lossless compression workflows to preserve detail across transformations

What happens if my prompt is ambiguous?

Returns a ranked gallery of 3 variations for selection, with metadata explaining differences

Gemini Image Generator MCP Server

Generate high-quality images from text prompts using Google's Gemini model through the MCP protocol.

Overview

This MCP server allows any AI assistant to generate images using Google's Gemini AI model. The server handles prompt engineering, text-to-image conversion, filename generation, and local image storage, making it easy to create and manage AI-generated images through any MCP client.

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 based on prompts
• Automatic translation of non-English prompts
• Local image storage with configurable output path
• Strict text exclusion from generated images
• High-resolution image output

Available MCP Tools

The server provides the following MCP tools for AI assistants:

1. generate_image_from_text

Creates a new image from a text prompt description.

generate_image_from_text(prompt: str) -> str

Parameters:

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

Returns:

• Path to the generated image file

Example:

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

2. 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) -> 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:

• Path to the transformed image file

Example:

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

3. transform_image_from_file

Transforms an existing image file based on a text prompt.

transform_image_from_file(image_file_path: str, prompt: str) -> 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:

• Path to the transformed image file

Example:

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

Setup

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. You can check your usage in the Google AI Studio

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 to include your Google Gemini API key and preferred output path:

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"
  }
  }
  }
  }

Usage

Once installed and configured, you can ask Claude to generate or transform images using 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"

The generated/transformed images will be saved to your configured output path and displayed in Claude.

Testing

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

fastmcp dev server.py

This command starts a local development server and makes the MCP Inspector available at http://localhost:5173/. The MCP Inspector provides a convenient web interface where you can directly test the image generation tool without needing to use Claude or another MCP client. You can enter text prompts, execute the tool, and see the results immediately, which is helpful for development and debugging.

License

MIT License