🎨 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

Tool	Description
check_api_status	Verify API key configuration and connectivity
list_image_models	Discover available image generation models
set_image_model	Select which model to use for generation
get_current_image_model	Check which model is currently selected
generate_image_from_prompt	Generate images from text descriptions
generate_image_with_references_from_files	Generate using 1–3 reference images (can be included as actual content, as-is or modified per prompt)
generate_image_resized_from_prompt	Generate an image then resize/compress to target bounds
generate_image_with_references_resized_from_files	Generate with references then resize/compress
save_image_to_file	Save generated images to the filesystem
generate_and_save_image	Generate and save in a single operation
generate_and_save_image_resized	Generate, resize/compress, and save an optimized output
generate_and_save_image_with_references	Generate with references and save in one step
generate_and_save_image_with_references_resized	Generate with references, resize/compress, and save
convert_image	Convert formats (png, jpeg, webp, heic/heif, ico) with favicon sizing

🔧 Prerequisites

• Python 3.9+ (uses standard library features available in 3.9+)
• Google AI API Key (Get one here)
• Pillow (installed automatically via requirements.txt for resizing/optimization)
• pillow-heif (installed via requirements.txt for HEIC/HEIF support)
• 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

Variable	Description	Required
GOOGLE_AI_API_KEY	Google AI API key	✅ Yes
IMAGEN_MODEL_ID	Default model to use (defaults to gemini-3-pro-image-preview)	❌ No

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

Aspect Ratio	Use Case
1:1	Social media posts, profile pictures
3:2, 2:3	Photography, prints
4:3, 3:4	Traditional displays
4:5, 5:4	Instagram posts
16:9, 9:16	Widescreen, mobile stories
21:9	Ultra-wide, cinematic

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:

Parameter	Type	Required	Description
model_name	string	✅	Model ID from list_image_models

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:

Parameter	Type	Required	Description
prompt	string	✅	Detailed text description of the image
aspect_ratio	string	❌	One of the supported aspect ratios
model	string	❌	Override the current model

Returns:

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

---

generate_image_with_references_from_files

Generate an image using 1–3 reference images (files on disk) plus a text prompt.

Important: reference images are visual inputs — you can instruct the model to include the referenced object/subject inside the generated image (as-is or modified), not only copy its style.

Parameters:

Parameter	Type	Required	Description
reference_paths	string[]	✅	1–3 paths to reference images (order matters)
prompt	string	✅	Describe the output and how to use each reference (e.g., keep object identical vs modify)
aspect_ratio	string	❌	Optional aspect ratio
model	string	❌	Override the current model

---

generate_image_with_references_resized_from_files

Same as generate_image_with_references_from_files, but additionally resizes/compresses to target bounds.

---

generate_and_save_image_with_references

Convenience tool that generates from references and saves to output_path (adds an extension if missing).

---

generate_and_save_image_with_references_resized

Convenience tool that generates from references, resizes/compresses, and saves to output_path.

---

save_image_to_file

Save a base64-encoded image to a file.

Parameters:

Parameter	Type	Required	Description
image_base64	string	✅	Base64-encoded image data
output_path	string	✅	File path to save the image

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:

Parameter	Type	Required	Description
prompt	string	✅	Detailed text description of the image
output_path	string	✅	File path to save the image
aspect_ratio	string	❌	One of the supported aspect ratios
model	string	❌	Override the current model

Returns:

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

---

generate_image_resized_from_prompt

Generate an image, then resize/compress it to fit within given dimensions.

Parameters:

Parameter	Type	Required	Description
prompt	string	✅	Detailed text description of the image
max_width	integer	✅	Target max width in pixels
max_height	integer	✅	Target max height in pixels
aspect_ratio	string	❌	One of the supported aspect ratios
model	string	❌	Override the current model
format	string	❌	Output format (png, jpeg, webp; defaults to source/PNG)
quality	integer	❌	Quality 1-100 (applies to JPEG/WEBP)

Returns:

{
  "success": true,
  "image_base64": "iVBORw0KGgo...",
  "mime_type": "image/jpeg",
  "extension": ".jpg",
  "size_bytes": 123456,
  "model_used": "gemini-3-pro-image-preview",
  "resized": true,
  "max_width": 1024,
  "max_height": 1024
}

---

generate_and_save_image_resized

Generate an image, resize/compress it, and save to disk (kept separate from the high-res save path).

Parameters:

Parameter	Type	Required	Description
prompt	string	✅	Detailed text description of the image
output_path	string	✅	File path to save; extension inferred if missing
max_width	integer	✅	Target max width in pixels
max_height	integer	✅	Target max height in pixels
aspect_ratio	string	❌	One of the supported aspect ratios
model	string	❌	Override the current model
format	string	❌	Output format (png, jpeg, webp; defaults to source/PNG)
quality	integer	❌	Quality 1-100 (applies to JPEG/WEBP)

Returns:

{
  "success": true,
  "saved_path": "/absolute/path/to/image.jpg",
  "mime_type": "image/jpeg",
  "size_bytes": 123456,
  "model_used": "gemini-3-pro-image-preview",
  "resized": true,
  "max_width": 1024,
  "max_height": 1024
}

---

convert_image

Convert an image to another format, optionally emitting multi-size ICOs for favicons.

Parameters:

Parameter	Type	Required	Description
input_path	string	✅	Source image path
output_path	string	✅	Destination path (extension may be inferred from format)
format	string	✅	One of png, jpeg/jpg, webp, heic/heif, ico
sizes	array<int>	❌	For ICO: list of sizes (e.g., [16,32,48,64,128]); ignored for other formats

Returns:

{
  "success": true,
  "saved_path": "/absolute/path/to/favicon.ico",
  "mime_type": "image/x-icon",
  "sizes": [16,32,48,64,128],
  "format": "ico"
}

💡 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
├── vscode-extension/        # VS Code extension to manage MCP config
│   ├── package.json
│   ├── tsconfig.json
│   └── src/extension.ts

🧩 VS Code Extension (Optional)

You can manage the MCP server from inside VS Code via the bundled extension.

Build & Install

For End Users (Marketplace install — auto updates)

• Install from the VS Code Marketplace (search “Imagen MCP Server”). Marketplace installs auto-update with new releases.
• After install: run the commands below to set your API key and model.

For Manual / VSIX Install

1. cd vscode-extension
2. npm install
3. npm run package (creates imagen-mcp-vscode-<version>.vsix)
4. In VS Code, run “Extensions: Install from VSIX...” and pick the .vsix (updates require installing the new VSIX).

For Contributors (publish a release)

1. Set publisher in vscode-extension/package.json (already gramini-consulting).
2. Set VSCE_PAT (Personal Access Token with Marketplace publish rights).
3. cd vscode-extension && npm install && npm run package && npx vsce publish (bumps version before publish as needed).
4. Tag the release in GitHub and attach the .vsix for non-marketplace installs.

Commands (Command Palette)

• Imagen MCP: Set API Key – stored securely in VS Code Secret Storage.
• Imagen MCP: Select Model – updates workspace setting imagenMcp.modelId (default gemini-3-pro-image-preview).
• Imagen MCP: Generate MCP Config – writes .vscode/mcp.json wiring the server command/args and env (GOOGLE_AI_API_KEY from secrets, IMAGEN_MODEL_ID from settings, falls back to built-in default).

Extension Settings

• imagenMcp.modelId (default gemini-3-pro-image-preview)
• imagenMcp.serverCommand (default python)
• imagenMcp.serverArgs (default ["${workspaceFolder}/run_server.py"])

Tip: set imagenMcp.serverCommand to ./run_with_venv.sh if you prefer the helper script; arguments are typically empty in that case.

🛡️ 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

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