Image Generation MCP Server

Image Generation MCP Server

By MCP-Mirror GitHub

Mirror of

image-generation mcp-server
Overview

what is Image Generation MCP Server?

Image Generation MCP Server is a Model Context Protocol (MCP) server that enables seamless generation of high-quality images using the Flux.1 Schnell model via Together AI. It provides a standardized interface for specifying image generation parameters.

how to use Image Generation MCP Server?

To use the server, install it via npm and configure it with your Together AI API key. You can then call the generate_image tool with a prompt to generate images.

key features of Image Generation MCP Server?

  • High-quality image generation powered by the Flux.1 Schnell model
  • Customizable dimensions for generated images
  • Clear error handling for prompt validation and API issues
  • Easy integration with MCP-compatible clients
  • Option to save generated images in PNG format

use cases of Image Generation MCP Server?

  1. Generating artistic images based on textual descriptions.
  2. Creating images for marketing materials or social media posts.
  3. Assisting designers in visualizing concepts quickly.

FAQ from Image Generation MCP Server?

  • What is the required parameter for generating an image?

The only required parameter is the prompt, which describes the image to generate.

  • Can I customize the dimensions of the generated images?

Yes, you can specify the width and height of the images.

  • Is there a way to save the generated images?

Yes, you can specify a path to save the generated image as a PNG file.

Content

Image Generation MCP Server

A Model Context Protocol (MCP) server that enables seamless generation of high-quality images using the Flux.1 Schnell model via Together AI. This server provides a standardized interface to specify image generation parameters.

Image Generation Server MCP server

Features

  • High-quality image generation powered by the Flux.1 Schnell model
  • Support for customizable dimensions (width and height)
  • Clear error handling for prompt validation and API issues
  • Easy integration with MCP-compatible clients
  • Optional image saving to disk in PNG format

Installation

npm install together-mcp

Or run directly:

npx together-mcp@latest

Configuration

Add to your MCP server configuration:

Configuration Example
{
  "mcpServers": {
    "together-image-gen": {
      "command": "npx",
      "args": ["together-mcp@latest -y"],
      "env": {
        "TOGETHER_API_KEY": "<API KEY>"
      }
    }
  }
}

Usage

The server provides one tool: generate_image

Using generate_image

This tool has only one required parameter - the prompt. All other parameters are optional and use sensible defaults if not provided.

Parameters

{
  // Required
  prompt: string;          // Text description of the image to generate

  // Optional with defaults
  model?: string;          // Default: "black-forest-labs/FLUX.1-schnell-Free"
  width?: number;          // Default: 1024 (min: 128, max: 2048)
  height?: number;         // Default: 768 (min: 128, max: 2048)
  steps?: number;          // Default: 1 (min: 1, max: 100)
  n?: number;             // Default: 1 (max: 4)
  response_format?: string; // Default: "b64_json" (options: ["b64_json", "url"])
  image_path?: string;     // Optional: Path to save the generated image as PNG
}

Minimal Request Example

Only the prompt is required:

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset"
  }
}

Full Request Example with Image Saving

Override any defaults and specify a path to save the image:

{
  "name": "generate_image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset",
    "width": 1024,
    "height": 768,
    "steps": 20,
    "n": 1,
    "response_format": "b64_json",
    "model": "black-forest-labs/FLUX.1-schnell-Free",
    "image_path": "/path/to/save/image.png"
  }
}

Response Format

The response will be a JSON object containing:

{
  "id": string,        // Generation ID
  "model": string,     // Model used
  "object": "list",
  "data": [
    {
      "timings": {
        "inference": number  // Time taken for inference
      },
      "index": number,      // Image index
      "b64_json": string    // Base64 encoded image data (if response_format is "b64_json")
      // OR
      "url": string        // URL to generated image (if response_format is "url")
    }
  ]
}

If image_path was provided and the save was successful, the response will include confirmation of the save location.

Default Values

If not specified in the request, these defaults are used:

  • model: "black-forest-labs/FLUX.1-schnell-Free"
  • width: 1024
  • height: 768
  • steps: 1
  • n: 1
  • response_format: "b64_json"

Important Notes

  1. Only the prompt parameter is required
  2. All optional parameters use defaults if not provided
  3. When provided, parameters must meet their constraints (e.g., width/height ranges)
  4. Base64 responses can be large - use URL format for larger images
  5. When saving images, ensure the specified directory exists and is writable

Prerequisites

  • Node.js >= 16
  • Together AI API key
    1. Sign in at api.together.xyz
    2. Navigate to API Keys settings
    3. Click "Create" to generate a new API key
    4. Copy the generated key for use in your MCP configuration

Dependencies

{
  "@modelcontextprotocol/sdk": "0.6.0",
  "axios": "^1.6.7"
}

Development

Clone and build the project:

git clone https://github.com/manascb1344/together-mcp-server
cd together-mcp-server
npm install
npm run build

Available Scripts

  • npm run build - Build the TypeScript project
  • npm run watch - Watch for changes and rebuild
  • npm run inspector - Run MCP inspector

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository
  2. Create a new branch (feature/my-new-feature)
  3. Commit your changes
  4. Push the branch to your fork
  5. Open a Pull Request

Feature requests and bug reports can be submitted via GitHub Issues. Please check existing issues before creating a new one.

For significant changes, please open an issue first to discuss your proposed changes.

License

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

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