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Together AI Image Server

by zym9863
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Juntos AI Image Server

Inglés | Chino tradicional

Un servidor MCP (Protocolo de contexto de modelo) basado en TypeScript para generar imágenes utilizando la API Together AI.

Descripción general

Este servidor proporciona una interfaz sencilla para generar imágenes utilizando los modelos de Together AI mediante el protocolo MCP. Permite a Claude y a otros asistentes compatibles con MCP generar imágenes a partir de indicaciones de texto.

Related MCP server: gemini-nano-banana-mcp

Características

Herramientas

  • generate_image - Genera imágenes a partir de indicaciones de texto

    • Toma un mensaje de texto como parámetro obligatorio

    • Parámetros opcionales para controlar los pasos de generación y el número de imágenes

    • Devuelve URL y rutas locales a las imágenes generadas

Prerrequisitos

  • Node.js (se recomienda v14 o posterior)

  • Clave API de Together AI

Instalación

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

# Install dependencies
npm install

Configuración

Establezca su clave API de Together AI como una variable de entorno:

# 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"

Alternativamente, puede crear un archivo .env en la raíz del proyecto:

TOGETHER_API_KEY=your-api-key-here

Desarrollo

Construir el servidor:

npm run build

Para desarrollo con reconstrucción automática:

npm run watch

Uso con Claude Desktop

Para utilizar con Claude Desktop, agregue la configuración del servidor:

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

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

Reemplace /path/to/together-ai-image-server con la ruta real a su instalación.

Depuración

Dado que los servidores MCP se comunican a través de stdio, la depuración puede ser complicada. Recomendamos usar el Inspector MCP , disponible como script de paquete:

npm run inspector

El Inspector proporcionará una URL para acceder a las herramientas de depuración en su navegador.

Referencia de API

generar_imagen

Genera imágenes basadas en una solicitud de texto utilizando la API de generación de imágenes de Together AI.

Parámetros:

  • prompt (cadena, obligatorio): mensaje de texto para la generación de imágenes

  • steps (número, opcional, predeterminado: 4): Número de pasos de difusión (1-4)

  • n (número, opcional, predeterminado: 1): Número de imágenes a generar (1-4)

Devoluciones:

Objeto JSON que contiene:

  • image_urls : Matriz de URL de las imágenes generadas

  • local_paths : Matriz de rutas a imágenes almacenadas en caché local

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Contribuyendo

¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.

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

Resources

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