AI Image Generation Server with MCP Interface

@aymec

About AI Image Generation Server with MCP Interface

This project provides an HTTP server for image generation using Stable Diffusion, along with a Model Context Protocol (MCP) server that enables AI agents to request image generation.

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "image_gen_with_mcp": {
      "command": "python",
      "args": [
        "generate_image.py"
      ]
    }
  }
}

Tools

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Overview

What is AI Image Generation Server with MCP Interface?

This project is an MCP (Model Context Protocol) server with integrated Stable Diffusion image generation capabilities. It enables AI agents to request and receive generated images via a standardized MCP interface. It is based on Block's Goose Custom Extension tutorial.

How to use AI Image Generation Server with MCP Interface?

Set up a virtual environment (mandatorily named .venv), install the MCP package (pip install -e .), then run the combined service with image-gen-mcp after setting the IMAGE_GEN_DIR environment variable. Alternatively, use development mode with the FastMCP Inspector for testing and debugging. Integrate with Goose as a StandardIO extension by providing the executable path and the IMAGE_GEN_DIR variable.

Key features of AI Image Generation Server with MCP Interface

Combined MCP server and image generation service
Uses Stable Diffusion for text-to-image generation
Provides a direct HTTP API for image generation
Returns image URL, dimensions, format, and metadata
Runs on port 5000 by default (customizable)
Supports integration with Goose and the FastMCP Inspector

Use cases of AI Image Generation Server with MCP Interface

AI agents generating images based on user prompts
Testing and debugging image generation with FastMCP Inspector
Integrating image generation into custom AI workflows via MCP
Running local image generation with Ollama (tested with mistral-small3.1)
Serving generated images via HTTP for direct access

FAQ from AI Image Generation Server with MCP Interface

What runtime does it require?

It requires Python 3 and a virtual environment. The project uses the mcp package and Stable Diffusion dependencies installed via pip install -e ..

How do I set the output folder for images?

Set the environment variable IMAGE_GEN_DIR to the absolute path of the target folder before starting the service.

Can I change the port for the image generation service?

Yes, use the --port flag when running image-gen-mcp (e.g., image-gen-mcp --port 5001).

Does it work with local models like Ollama?

Yes, it can run locally with Ollama. The only model tested successfully is mistral-small3.1, though it is slower than using OpenAI GPT-4o.

How do I stop the service?

Press Ctrl+C to stop both the MCP server and the image generation service, as they run within the same process.

Frequently asked questions

What runtime does it require?

It requires Python 3 and a virtual environment. The project uses the `mcp` package and Stable Diffusion dependencies installed via `pip install -e .`.

How do I set the output folder for images?

Set the environment variable `IMAGE_GEN_DIR` to the absolute path of the target folder before starting the service.

Can I change the port for the image generation service?

Yes, use the `--port` flag when running `image-gen-mcp` (e.g., `image-gen-mcp --port 5001`).

Does it work with local models like Ollama?

Yes, it can run locally with Ollama. The only model tested successfully is mistral-small3.1, though it is slower than using OpenAI GPT-4o.

How do I stop the service?

Press Ctrl+C to stop both the MCP server and the image generation service, as they run within the same process.

Comments

Basic information

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