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manascb1344

Image Generation MCP Server

by manascb1344
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JavaScript
Hybrid

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

Related MCP server: Image Generation MCP Server

Installation

npm install together-mcp

Or run directly:

npx together-mcp@latest

Configuration

Add to your MCP server configuration:

{
  "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.

Available Tools

1 tool
generate_imageC

Generate an image using Together AI API

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for image generation
modelNoModel to use for generation (default: black-forest-labs/FLUX.1-schnell-Free)
widthNoImage width (default: 1024)
heightNoImage height (default: 768)
stepsNoNumber of inference steps (default: 1)
nNoNumber of images to generate (default: 1)
response_formatNoResponse format (default: b64_json)
image_pathNoOptional path to save the generated image as PNG

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API provider but fails to describe critical behaviors like rate limits, authentication requirements, cost implications, error handling, or what happens when saving to 'image_path'. This leaves significant gaps for a tool with 8 parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with a single sentence that directly states the tool's purpose. There is zero wasted language, and it's front-loaded with the core functionality, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (8 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with incomplete information for proper tool invocation in a real-world context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, meeting the baseline score of 3 for high schema coverage without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Generate an image') and the target resource ('using Together AI API'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or context for invocation. It simply states what the tool does without any usage instructions or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.7
    • First observedgenerate_image
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.

Tool Count2/5

A single tool is too few for a server named 'Image Generation MCP Server', which suggests a broader scope. While the tool covers basic generation, the lack of additional tools (e.g., for editing, listing, or managing images) makes the surface feel thin and incomplete for the implied domain.

Completeness2/5

The server is severely incomplete for an image generation domain. It only provides a generate_image tool, with no coverage for related operations like listing generated images, editing parameters, deleting images, or handling variations. This creates significant gaps that will likely cause agent failures in broader workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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