Imagen MCP Server

A high-quality Model Context Protocol (MCP) server that enables AI assistants to generate images using Google's Gemini and Imagen models.

Overview

Imagen MCP provides AI-powered image generation capabilities to any MCP-compatible client (such as Claude Desktop, VS Code with GitHub Copilot, or custom applications). It connects to Google's AI platform to provide access to cutting-edge image generation models.

Why Use This MCP Server?

• Dynamic Model Selection: Query available models and choose the best one for your needs
• High-Quality Output: Access to Gemini and Imagen models for 2K/4K resolution images
• Flexible Aspect Ratios: Support for multiple aspect ratios (1:1, 16:9, 9:16, etc.)
• Text Rendering: Strong text-in-image rendering with Gemini models
• Secure Configuration: API keys stored securely via environment variables
• Easy Integration: Works with any MCP-compatible AI assistant
• Minimal Dependencies: Only requires fastmcp - all other functionality uses Python standard library

Features

Prerequisites

• Python 3.9+ (uses standard library features available in 3.9+)
• Google AI API Key (Get one here)
• An MCP-compatible client (Claude Desktop, VS Code with Copilot, etc.)

Quick Start

1. Clone the Repository

git clone https://github.com/yourusername/imagen-mcp.git
cd imagen-mcp

2. Install Dependencies

pip install -r requirements.txt

Or using a virtual environment (recommended):

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

3. Configure API Key

Create a .env file in the project root:

GOOGLE_AI_API_KEY=your_google_ai_api_key_here

Or set it as an environment variable directly in your MCP client configuration.

Tip: Get your API key from Google AI Studio

4. Test the Server

python run_server.py

Configuration

Environment Variables

Model selection fallback (highest priority first): explicit tool parameter runtime set_image_model IMAGEN_MODEL_ID env var built-in default gemini-3-pro-image-preview.

Supported Aspect Ratios

Note: Not all models support all aspect ratios. The server will automatically retry without aspect ratio if not supported.

MCP Client Integration

Claude Desktop

Add to your Claude Desktop configuration file:

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

{
  "mcpServers": {
    "imagen": {
      "command": "python",
      "args": ["/absolute/path/to/imagen-mcp/run_server.py"],
      "env": {
        "GOOGLE_AI_API_KEY": "your_api_key_here"
      }
    }
  }
}

VS Code with GitHub Copilot

Add to your VS Code MCP settings (.vscode/mcp.json or user settings):

{
  "servers": {
    "imagen": {
      "command": "python",
      "args": ["${workspaceFolder}/run_server.py"],
      "env": {
        "GOOGLE_AI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Or run the VS Code command: MCP: Open User Configuration and add the server.

Using with uv (Recommended for Isolation)

If you have uv installed:

{
  "mcpServers": {
    "imagen": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/imagen-mcp", "python", "run_server.py"],
      "env": {
        "GOOGLE_AI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Tools Reference

check_api_status

Verify that your API key is configured and working.

Parameters: None

Returns:

{
  "success": true,
  "api_key_configured": true,
  "api_key_valid": true,
  "total_models": 25,
  "image_models": 3,
  "current_model": "gemini-3-pro-image-preview"
}

list_image_models

Discover available image generation models for your API key.

Parameters: None

Returns:

{
  "success": true,
  "models": [
    {
      "name": "gemini-3-pro-image-preview",
      "display_name": "Gemini 3 Pro (Image Preview)",
      "description": "Fast image generation model..."
    }
  ],
  "current_model": null,
  "count": 3
}

set_image_model

Select which model to use for image generation.

Parameters:

Returns:

{
  "success": true,
  "model": "gemini-3-pro-image-preview",
  "message": "Model set to 'gemini-3-pro-image-preview'. Ready for image generation."
}

generate_image_from_prompt

Generate an image from a text description.

Parameters:

Returns:

{
  "success": true,
  "image_base64": "iVBORw0KGgo...",
  "mime_type": "image/png",
  "extension": ".png",
  "size_bytes": 1234567,
  "model_used": "gemini-3-pro-image-preview"
}

save_image_to_file

Save a base64-encoded image to a file.

Parameters:

Returns:

{
  "success": true,
  "saved_path": "/absolute/path/to/image.png",
  "size_bytes": 1234567
}

generate_and_save_image

Generate an image and save it to a file in one operation.

Parameters:

Returns:

{
  "success": true,
  "saved_path": "/absolute/path/to/image.png",
  "mime_type": "image/png",
  "size_bytes": 1234567,
  "model_used": "gemini-3-pro-image-preview"
}

Usage Examples

Once the server is connected to your AI assistant, you can use natural language:

First-Time Setup

"Check if my API key is configured correctly"
"List available image generation models"
"Set the model to gemini-2.0-flash-exp-image-generation"

Basic Image Generation

"Generate a sunset over mountains with vibrant orange and purple colors"

Product Photography

"Create a product shot of a smartwatch on a minimalist white surface with dramatic lighting"

Specific Dimensions

"Generate a 16:9 banner image for a tech blog featuring abstract circuit patterns"

Save to Project

"Generate a hero image for my website and save it to assets/images/hero.png"

Project Structure

imagen-mcp/
 image_generator/
    __init__.py          # Package initialization
    core.py              # Core image generation & model listing logic
    server.py            # MCP server implementation with tools
 run_server.py            # Server entry point
 run_with_venv.sh         # Helper script for venv
 requirements.txt         # Python dependencies (minimal)
 .env.example             # Example environment configuration
 LICENSE                  # MIT License
 README.md                # This file
 CONTRIBUTING.md          # Contribution guidelines

Security Considerations

• API Key Protection: Never commit your API key. Use environment variables or .env files
• Secure Storage: The .env file is included in .gitignore by default
• MCP Configuration: API keys can be passed securely via MCP client env configuration
• File System Access: Be mindful of where images are saved

Troubleshooting

Common Issues

"Missing API key" error

• Ensure GOOGLE_AI_API_KEY is set in your environment or .env file
• Check that the .env file is in the project root directory
• Verify the key is passed in your MCP client configuration

"No model selected" error

• Use list_image_models to see available models
• Use set_image_model to select one before generating

"Aspect ratio is not enabled" error

• The server automatically retries without aspect ratio
• Some models don't support custom aspect ratios

No image models found

• Your API key may not have access to image generation models
• Check your Google AI Studio account for API access

Connection issues with MCP client

• Verify the path in your MCP configuration is absolute
• Check that Python is in your system PATH
• Ensure all dependencies are installed

Debugging

Check your MCP client's logs:

• Claude Desktop: Check the application logs
• VS Code: View Output panel MCP

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Ways to Contribute

• Report bugs and issues
• Suggest new features
• Improve documentation
• Submit pull requests

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 Vipin Ravindran

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

Acknowledgments

• Model Context Protocol - The protocol specification
• FastMCP - Python MCP framework
• Google Gemini - Image generation models

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions