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Together AI Image Server

by zym9863
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TypeScript
Remote

Together AI Image Server

English | 简体中文

A TypeScript-based MCP (Model Context Protocol) server for generating images using Together AI API.

Overview

This server provides a simple interface to generate images using Together AI's image generation models through the MCP protocol. It allows Claude and other MCP-compatible assistants to generate images based on text prompts.

Related MCP server: gemini-nano-banana-mcp

Features

Tools

  • generate_image - Generate images from text prompts

    • Takes a text prompt as required parameter

    • Optional parameters for controlling generation steps and number of images

    • Returns URLs and local paths to generated images

Prerequisites

  • Node.js (v14 or later recommended)

  • Together AI API key

Installation

# Clone the repository
git clone https://github.com/zym9863/together-ai-image-server.git
cd together-ai-image-server

# Install dependencies
npm install

Configuration

Set your Together AI API key as an environment variable:

# On Linux/macOS
export TOGETHER_API_KEY="your-api-key-here"

# On Windows (Command Prompt)
set TOGETHER_API_KEY=your-api-key-here

# On Windows (PowerShell)
$env:TOGETHER_API_KEY="your-api-key-here"

Alternatively, you can create a .env file in the project root:

TOGETHER_API_KEY=your-api-key-here

Development

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Usage with Claude Desktop

To use with Claude Desktop, add the server config:

On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "Together AI Image Server": {
      "command": "/path/to/together-ai-image-server/build/index.js"
    }
  }
}

Replace /path/to/together-ai-image-server with the actual path to your installation.

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

API Reference

generate_image

Generates images based on a text prompt using Together AI's image generation API.

Parameters:

  • prompt (string, required): Text prompt for image generation

  • steps (number, optional, default: 4): Number of diffusion steps (1-4)

  • n (number, optional, default: 1): Number of images to generate (1-4)

Returns:

JSON object containing:

  • image_urls: Array of URLs to the generated images

  • local_paths: Array of paths to locally cached images

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Available Tools

1 tool
generate_imageC

Generate image from text prompt using Together AI API

ParametersJSON Schema
NameRequiredDescriptionDefault
nNoNumber of images to generate (default: 1, max: 4)
stepsNoNumber of diffusion steps (default: 4)
promptYesText prompt for image generation

TDQS

C2.9/5.0
Behavior1/5

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

No annotations are provided, so the description carries full burden. It only mentions the external API but does not disclose any behavioral traits such as rate limits, authentication needs, what happens under the hood, or potential side effects like image generation limits or API costs.

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

Conciseness4/5

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

The description is a single sentence that is concise and front-loaded with the core action. However, it is too short to cover necessary details, but for what it states, it is 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 absence of an output schema and annotations, the description is incomplete. It provides no information about what the tool returns (e.g., image URLs or base64), any limitations, or error conditions. The user would need to guess or rely on external knowledge.

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?

All three parameters have descriptions in the input schema (100% coverage). The description adds no extra meaning beyond the schema, which already explains 'prompt', 'n', and 'steps'. Baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool's purpose: generating an image from a text prompt using the Together AI API. The verb 'generate' and resource 'image' are specific, and mentioning the API adds context. No siblings exist, so differentiation is not needed.

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?

No guidance is provided on when to use or avoid this tool. There is no mention of prerequisites, alternatives, or when not to use it. The description simply states what it does without contextual usage advice.

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

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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined.

Naming Consistency5/5

The single tool 'generate_image' follows a clear verb_noun pattern, which is consistent by default.

Tool Count2/5

The server has only one tool, which is too few for a typical image generation service. Users would likely expect additional tools for model selection, image variants, or status retrieval.

Completeness2/5

The tool surface is severely incomplete; a comprehensive image generation server would typically include tools for listing models, configuring generation parameters, and possibly managing generated images.

Maintenance

ActivityInactive
ResponsivenessNo issues

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