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together-mcp-server

MCP.Pizza Chef: manascb1344

The together-mcp-server is an MCP server that enables seamless, high-quality image generation using Together AI's Flux.1 Schnell model. It offers a standardized interface for specifying image generation parameters such as dimensions, with robust error handling and optional PNG image saving. Designed for easy integration with MCP-compatible clients, it streamlines image creation workflows in AI-enhanced applications.

Use This MCP server To

Generate high-quality images from text prompts with customizable dimensions Integrate image generation into AI copilots and workflow automation Save generated images automatically in PNG format for further use Validate and handle errors in image generation prompts and API calls Enable real-time image creation in MCP-compatible development environments

README

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.

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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.

together-mcp-server FAQ

How do I install the together-mcp-server?
You can install it via npm using 'npm install together-mcp' or run it directly with 'npx together-mcp@...'.
What image formats does the server support for saving?
The server supports saving generated images in PNG format.
How does the server handle invalid prompts or API errors?
It includes clear error handling to validate prompts and manage API issues gracefully.
Can I customize the size of generated images?
Yes, the server supports customizable image dimensions including width and height.
Is the together-mcp-server compatible with other MCP clients?
Yes, it is designed for easy integration with any MCP-compatible client.
Which model powers the image generation?
The image generation is powered by Together AI's Flux.1 Schnell model.
Can this server be used in real-time applications?
Yes, it supports real-time image generation workflows within MCP environments.
Does the server support multiple concurrent image generation requests?
While not explicitly stated, MCP servers typically handle concurrent requests depending on deployment setup.