Which integrations are available for this server?

Enables text-to-image generation using [Google](/mcp/servers/integrations/google)'s Imagen 4.0 models, including standard, fast, and ultra quality variants, with support for style transfer using Imagen 3 Customization, customizable aspect ratios, and batch generation capabilities. Provides integration with [Google Cloud](/mcp/servers/integrations/google-cloud)'s Vertex AI for accessing Imagen API, supporting authentication via Google Cloud Default Application Credentials and project-based configuration for text-to-image generation and style transfer.

MCP Imagen Server

An MCP (Model Context Protocol) server for Google Imagen API, enabling text-to-image generation using Google's state-of-the-art Imagen models.

Features

Text-to-Image Generation: Generate high-quality images from text prompts using Imagen 4.0 models
Style Transfer: Generate images following the style of a reference image using Imagen 3 Customization
Background Removal: Remove backgrounds from images using rembg AI model
Auto-Crop: Automatically crop images to remove transparent or empty borders with batch processing support
Multiple Models: Support for three Imagen variants:
- imagen-4.0-generate-001 (default) - Standard quality and speed
- imagen-4.0-fast-generate-001 - Faster generation
- imagen-4.0-ultra-generate-001 - Highest quality (single image only)
Flexible Configuration:
- Customizable aspect ratios (1:1, 3:4, 4:3, 9:16, 16:9)
- Batch generation (1-4 images per request)
- PNG output format with transparency support
Authentication Options:
- Google Cloud Default Application Credentials
- Vertex AI or Gemini API

Prerequisites

Python 3.11 or later
uv for package management
Google Cloud credentials (see Authentication)

Installation

1. Clone the Repository

git clone https://github.com/anton-proto/mcp-imagen.git cd mcp-imagen/mcp-imagen-server

2. Install with uv

# Install dependencies uv sync # Or install in development mode uv sync --all-extras

3. Set Up Authentication

Option A: Using Gemini API (Recommended for quick start)

Get an API key from Google AI Studio
Set the API key:
export GOOGLE_API_KEY="your-api-key-here"

Option B: Using Vertex AI (Recommended for production)

Install Google Cloud SDK:
# For Debian/Ubuntu curl https://sdk.cloud.google.com | bash exec -l $SHELL
Authenticate with Google Cloud:
gcloud auth application-default login
Set your project:
export GOOGLE_CLOUD_PROJECT="your-project-id" export USE_VERTEXAI="true" # Optional: specify location (default: us-central1) export GOOGLE_CLOUD_LOCATION="us-central1"

Usage

Running the Server

uv run mcp-imagen-server

The server will start and listen for MCP requests via stdio.

Integration with Claude Desktop

Add this configuration to your Claude Desktop config file:

Location:

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

Configuration:

{ "mcpServers": { "imagen": { "command": "uv", "args": [ "--directory", "/absolute/path/to/mcp-imagen-server", "run", "mcp-imagen-server" ], "env": { "GOOGLE_API_KEY": "your-api-key-here" } } } }

For Vertex AI:

{ "mcpServers": { "imagen": { "command": "uv", "args": [ "--directory", "/absolute/path/to/mcp-imagen-server", "run", "mcp-imagen-server" ], "env": { "USE_VERTEXAI": "true", "GOOGLE_CLOUD_PROJECT": "your-project-id", "GOOGLE_CLOUD_LOCATION": "us-central1" } } } }

Integration with Other MCP Clients

The server implements the standard MCP protocol and can be used with any MCP-compatible client.

MCP Tools

text-to-image

Generates images from text prompts using Google Imagen API.

Parameters

Parameter	Type	Required	Default	Description
`prompt`	string	Yes	-	Text description of the image to generate
`output_dir`	string	Yes	-	Absolute path to directory where images should be saved
`model`	string	No	`imagen-4.0-generate-001`	Imagen model to use (see Models)
`sample_count`	integer	No	1	Number of images to generate (1-4, must be 1 for ultra model)
`aspect_ratio`	string	No	`1:1`	Aspect ratio of generated images (1:1, 3:4, 4:3, 9:16, 16:9)

Models

imagen-4.0-generate-001: Standard model with balanced quality and speed
imagen-4.0-fast-generate-001: Faster generation with good quality
imagen-4.0-ultra-generate-001: Highest quality, single image only (sample_count must be 1)

Response

Returns a text response with paths to generated PNG files:

Successfully generated 2 image(s): 1. /path/to/output/A_serene_mountain_landscape_at_sunset_1.png 2. /path/to/output/A_serene_mountain_landscape_at_sunset_2.png

Example Usage

In Claude Desktop or other MCP client:

Generate an image of "A serene mountain landscape at sunset with a lake reflecting the sky" and save it to /tmp/images/

style-to-image

Generates images following the style of a reference image using Imagen 3 Customization.

Parameters

Parameter	Type	Required	Description
`prompt`	string	Yes	Text description of the image content to generate
`style_image_path`	string	Yes	Absolute path to the style reference image file
`style_description`	string	Yes	Description of the style (e.g., "watercolor painting style", "neon sign style", "mosaic style")
`output_dir`	string	Yes	Absolute path to directory where images should be saved
`sample_count`	integer	No	Number of images to generate (1-4). Default: 1

Response

Returns a text response with paths to generated styled PNG files.

Example Usage

Generate an image of "A cat sitting on a windowsill" in the style of the image at /path/to/watercolor.png (watercolor painting style) and save it to /tmp/images/

remove-background

Removes the background from an image using the rembg AI model, producing a PNG with transparent background.

Parameters

Parameter	Type	Required	Description
`input_path`	string	Yes	Absolute path to the input image file
`output_path`	string	No	Absolute path to save the output image. If not provided, will save with 'nobg_' prefix in same directory

Response

Returns a text response with the path to the output image with transparent background:

Successfully removed background from image: Output: /path/to/output/nobg_image.png

Example Usage

Remove the background from /home/user/images/photo.png

Or with custom output path:

Remove the background from /home/user/images/photo.png and save it to /home/user/outputs/transparent.png

autocrop

Automatically crop images to remove transparent or empty borders. Supports both single image and batch processing with parallel execution.

Parameters

Parameter	Type	Required	Description
`input_paths`	array of strings	Yes	List of absolute paths to input image files to crop
`output_dir`	string	No	Absolute path to output directory. If not provided, cropped images will be saved in the same directory as input files with '_cropped' suffix
`padding`	integer	No	Number of pixels to add as padding around cropped content. Default: 0
`overwrite`	boolean	No	Whether to overwrite existing output files. Default: True. If False and output file exists, the operation will fail with an error

Response

Returns a text response with processing summary and paths to cropped images:

Processed 3 image(s): Successfully cropped: 3 Cropped images: 1. /output/dir/image1_cropped.png 2. /output/dir/image2_cropped.png 3. /output/dir/image3_cropped.png

If any images fail to process, they will be listed separately:

Processed 3 image(s): Successfully cropped: 2 Cropped images: 1. /output/dir/image1_cropped.png 2. /output/dir/image2_cropped.png Failed: 1 - image3.png: Error: Image appears to be completely transparent or empty - cannot autocrop

Features

Parallel Processing: Multiple images are processed concurrently for better performance
Batch Support: Process multiple images in a single call
Flexible Output: Save to a specific directory or use default location
Padding Control: Add padding around cropped content if needed
Transparency Aware: Automatically detects and crops around non-transparent pixels

Example Usage

Single image:

Autocrop the image at /home/user/images/logo.png

Multiple images with output directory:

Autocrop these images: ["/home/user/images/logo1.png", "/home/user/images/logo2.png", "/home/user/images/logo3.png"] and save to /home/user/cropped/

With padding:

Autocrop /home/user/images/logo.png with 10 pixels of padding and save to /home/user/output/

With overwrite disabled (prevent overwriting):

Autocrop /home/user/images/logo.png and save to /home/user/output/ with overwrite disabled

Note: By default, the tool will overwrite existing output files. Set overwrite=False to prevent accidental overwrites and raise an error if the output file already exists.

Development

Project Structure

mcp-imagen-server/ ├── src/ │ └── mcp_imagen_server/ │ ├── __init__.py # Package initialization │ ├── imagen_client.py # Imagen API client │ └── server.py # MCP server implementation ├── pyproject.toml # Project configuration ├── README.md # This file └── .python-version # Python version

Code Quality

The project uses ruff for linting and formatting:

# Check code uv run ruff check src/ # Format code uv run ruff format src/ # Auto-fix issues uv run ruff check --fix src/

Running Tests

# Run tests (when implemented) uv run pytest

Environment Variables

Variable	Description	Required	Default
`GOOGLE_API_KEY`	Google AI API key for Gemini API	For Gemini API	-
`USE_VERTEXAI`	Set to "true" to use Vertex AI	No	false
`GOOGLE_CLOUD_PROJECT`	GCP project ID	For Vertex AI	-
`GOOGLE_CLOUD_LOCATION`	GCP region	No	us-central1

Troubleshooting

Authentication Issues

Problem: DefaultCredentialsError or authentication failures

Solution:

For Gemini API: Ensure GOOGLE_API_KEY is set
For Vertex AI: Run gcloud auth application-default login
Verify your project has the Vertex AI API enabled

Permission Denied

Problem: Cannot write to output directory

Solution: Ensure the specified output_dir exists and is writable, or the server has permissions to create it

Model Not Available

Problem: Model not found or access denied

Solution:

Verify your Google Cloud project has access to Imagen models
Check that you're using a supported model name
For ultra model, ensure sample_count=1

License

MIT License - See LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

MCP Imagen Server

Features

Prerequisites

Installation

1. Clone the Repository

2. Install with uv

3. Set Up Authentication

Option A: Using Gemini API (Recommended for quick start)

Option B: Using Vertex AI (Recommended for production)

Usage

Running the Server

Integration with Claude Desktop

Integration with Other MCP Clients

MCP Tools

text-to-image

Parameters

Models

Response

Example Usage

style-to-image

Parameters

Response

Example Usage

remove-background

Parameters

Response

Example Usage

autocrop

Parameters

Response

Features

Example Usage

Development

Project Structure

Code Quality

Running Tests

Environment Variables

Troubleshooting

Authentication Issues

Permission Denied

Model Not Available

License

Contributing

References

Resources

New MCP Servers

Latest Blog Posts

MCP directory API