Which integrations are available for this server?

Enables image generation using Google's Imagen models (Imagen 4, Imagen 4 Fast, and Imagen 4 Ultra) for creating photorealistic images from text prompts with excellent prompt following capabilities. Provides access to OpenAI's image generation models (DALL-E 3 and GPT Image 1) for creating high-quality artistic images with strong prompt understanding through the Nexos.ai platform.

How do I use Imagen MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Imagen MCP Server generate a photorealistic sunset over mountains with imagen-4-ultra" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Imagen MCP Server

A Model Context Protocol (MCP) server for image generation using Google's Imagen model and other models supported by the Nexos.ai platform.

Features

Simple Image Generation: Generate a single image from a text prompt
Batch Image Generation: Generate multiple images with background processing
- First image is returned immediately
- Remaining images are generated in the background
- Query for additional images as they become available
Model Catalog: Access comprehensive information about all available models

Supported Models

Model	Provider	Description
`imagen-4`	Google	Flagship model with excellent prompt following and photorealistic output
`imagen-4-fast`	Google	Faster variant optimized for speed
`imagen-4-ultra`	Google	Highest quality for premium image generation
`dall-e-3`	OpenAI	High-quality model with excellent artistic capabilities
`gpt-image-1`	OpenAI	Strong prompt understanding and versatile output

Installation

Option 1: Install with pipx (Recommended for CLI usage)

# Install directly from the repository pipx install git+https://github.com/your-username/Imagen-MCP.git # Or install from local directory cd Imagen-MCP pipx install . # Run the server imagen-mcp

Option 2: Install with Poetry (Recommended for development)

# Clone the repository git clone <repository-url> cd Imagen-MCP # Install dependencies with Poetry poetry install # Run the server poetry run imagen-mcp # Or poetry run python -m Imagen_MCP.server

Option 3: Install with pip

# Install from the repository pip install git+https://github.com/your-username/Imagen-MCP.git # Or install from local directory pip install . # Run the server imagen-mcp

Environment Variables

Set up your Nexos.ai API key:

export NEXOS_API_KEY=your-api-key-here

Or create a .env file:

NEXOS_API_KEY=your-api-key-here

Usage

Running the Server

# If installed with pipx or pip imagen-mcp # If using Poetry (development) poetry run imagen-mcp # Alternative: run as Python module poetry run python -m Imagen_MCP.server # With FastMCP CLI (more options) poetry run fastmcp run Imagen_MCP/server.py --transport http --port 8000

CLI Options

When using the fastmcp run command, you have additional options:

Option	Description
`--transport`, `-t`	Transport protocol: `stdio` (default), `http`, `sse`, `streamable-http`
`--host`	Host to bind to (default: 127.0.0.1)
`--port`, `-p`	Port for HTTP/SSE transport (default: 8000)
`--log-level`, `-l`	Log level: DEBUG, INFO, WARNING, ERROR, CRITICAL
`--no-banner`	Don't show the server banner

MCP Client Configuration

To use this MCP server with an AI agent, add the following configuration to your MCP client.

Claude Desktop (pipx installation)

If you installed with pipx, add to your Claude Desktop configuration file (~/.config/claude/claude_desktop_config.json on Linux, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "imagen": { "command": "imagen-mcp", "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } } }

Claude Desktop (Poetry installation)

If you're using Poetry for development:

{ "mcpServers": { "imagen": { "command": "poetry", "args": ["run", "imagen-mcp"], "cwd": "/path/to/Imagen-MCP", "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } } }

Cline / Roo Code

Add to your VS Code settings or Cline MCP configuration:

{ "mcpServers": { "imagen": { "command": "imagen-mcp", "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } } }

Generic MCP Client (Copy-Paste Ready)

For pipx/pip installation:

{ "imagen": { "command": "imagen-mcp", "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } }

For Poetry installation:

{ "imagen": { "command": "poetry", "args": ["run", "imagen-mcp"], "cwd": "/path/to/Imagen-MCP", "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } }

Configuration Options:

Field	Description
`command`	The command to run (`poetry` for Poetry-managed projects)
`args`	Command arguments to start the MCP server
`cwd`	Working directory - set to your Imagen-MCP installation path
`env`	Environment variables, including the required `NEXOS_API_KEY`

Important: Replace /path/to/Imagen-MCP with the actual path to your Imagen-MCP installation and your-nexos-api-key-here with your Nexos.ai API key.

Alternative: Using pip-installed package

If you install the package globally or in a virtual environment:

{ "imagen": { "command": "python", "args": ["-m", "Imagen_MCP.server"], "env": { "NEXOS_API_KEY": "your-nexos-api-key-here" } } }

Tools

`list_models`

List all available image generation models with their descriptions, capabilities, and use cases.

Parameters: None

Returns:

models: List of all available models with details
total_count: Number of available models
default_model: The default model ID
usage_hint: How to use the model parameter

Example Response:

{ "models": [ { "id": "imagen-4", "name": "Imagen 4", "provider": "Google", "description": "Google's flagship image generation model...", "use_cases": ["Photorealistic image generation", ...], "strengths": ["Excellent prompt adherence", ...], "weaknesses": ["Slower generation time", ...], "supported_sizes": ["256x256", "512x512", "1024x1024", ...], "max_images_per_request": 4, "supports_hd_quality": true, "rate_limit": "100 messages per 3 hours" }, ... ], "total_count": 5, "default_model": "imagen-4" }

`get_model_details`

Get detailed information about a specific image generation model.

Parameters:

model_id (required): The model identifier (e.g., "imagen-4", "imagen-4-fast", "dall-e-3")

Returns:

Complete model details including capabilities, rate limits, use cases, strengths, and weaknesses
Error message if model not found

Example:

result = get_model_details(model_id="imagen-4-fast")

`generate_image`

Generate a single image from a text prompt. The image is saved to a file (temporary file if no path specified).

Parameters:

prompt (required): Text description of the image to generate
model (optional): Model to use (default: "imagen-4")
size (optional): Image size (default: "1024x1024")
quality (optional): Image quality - "standard" or "hd" (default: "standard")
style (optional): Image style - "vivid" or "natural" (default: "vivid")

Returns:

success: Whether the image was generated successfully
file_path: Absolute path to the saved image file
file_size_bytes: Size of the saved image file in bytes
model_used: The model that was used for generation
revised_prompt: The revised prompt (if the model modified it)
error: Error message if generation failed

Example:

result = await generate_image( prompt="A serene mountain landscape at sunset", model="imagen-4", size="1024x1024", quality="hd", style="natural" ) if result.success: print(f"Image saved to: {result.file_path}") print(f"File size: {result.file_size_bytes} bytes")

`start_image_batch`

Start generating multiple images and return the first one immediately. Images are saved to files (in a temporary directory if no path specified).

Parameters:

prompt (required): Text description of the image to generate
count (optional): Number of images to generate, 2-10 (default: 4)
model (optional): Model to use (default: "imagen-4")
size (optional): Image size (default: "1024x1024")
quality (optional): Image quality (default: "standard")
style (optional): Image style (default: "vivid")

Returns:

success: Whether the batch was started successfully
session_id: ID for retrieving more images
first_image_path: Path to the first generated image file
first_image_size_bytes: Size of the first image file in bytes
pending_count: Number of images still being generated
error: Error message if batch failed to start

Example:

result = await start_image_batch( prompt="A futuristic cityscape", count=5, model="imagen-4" ) if result.success: print(f"Session ID: {result.session_id}") print(f"First image: {result.first_image_path}")

`get_next_image`

Get the next available image from a batch generation session. The image is saved to a file (temporary file if no path specified).

Parameters:

session_id (required): Session ID from start_image_batch
timeout (optional): Maximum wait time in seconds (default: 60)

Returns:

success: Whether an image was retrieved
file_path: Path to the saved image file (or null if no image available)
file_size_bytes: Size of the saved image file in bytes
has_more: Whether more images are available or pending
pending_count: Number of images still being generated
error: Error message if retrieval failed

Example:

while True: result = await get_next_image(session_id=session_id) if result.file_path: print(f"Image saved to: {result.file_path}") if not result.has_more: break

`get_batch_status`

Get the current status of a batch generation session.

Parameters:

session_id (required): Session ID from start_image_batch

Returns:

status: Session status (created, generating, partial, completed, failed)
completed_count: Number of completed images
pending_count: Number of pending images
total_count: Total number of requested images
errors: List of any errors encountered

Resources

`models://image-generation`

Get the complete catalog of available image generation models with their capabilities, rate limits, use cases, strengths, and weaknesses.

`models://image-generation/{model_id}`

Get detailed information about a specific model.

Development

Running Tests

# Run all tests poetry run pytest # Run with verbose output poetry run pytest -v # Run specific test file poetry run pytest tests/unit/test_generate_image.py

Project Structure

Imagen_MCP/ ├── __init__.py # Package exports ├── server.py # FastMCP server definition ├── config.py # Configuration management ├── constants.py # Constants and type definitions ├── exceptions.py # Custom exceptions ├── tools/ │ ├── generate_image.py # Simple image generation tool │ └── batch_generate.py # Batch generation tools ├── resources/ │ └── models.py # Model catalog resource ├── services/ │ ├── nexos_client.py # Nexos.ai API client │ ├── session_manager.py # Background generation session manager │ └── model_registry.py # Model information registry └── models/ ├── image.py # Image data models ├── generation.py # Generation request/response models └── session.py # Session state models

Rate Limits

All models are in Category 3 on Nexos.ai:

100 messages per 3 hours

License

MIT License

Imagen MCP Server

Imagen MCP Server

Features

Supported Models

Installation

Option 1: Install with pipx (Recommended for CLI usage)

Option 2: Install with Poetry (Recommended for development)

Option 3: Install with pip

Environment Variables

Usage

Running the Server

CLI Options

MCP Client Configuration

Claude Desktop (pipx installation)

Claude Desktop (Poetry installation)

Cline / Roo Code

Generic MCP Client (Copy-Paste Ready)

Alternative: Using pip-installed package

Tools

`list_models`

`get_model_details`

`generate_image`

`start_image_batch`

`get_next_image`

`get_batch_status`

Resources

`models://image-generation`

`models://image-generation/{model_id}`

Development

Running Tests

Project Structure

Rate Limits

License

Resources

Tools

New MCP Servers

Latest Blog Posts

MCP directory API