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manascb1344

Image Generation MCP Server

by manascb1344

Servidor MCP de generación de imágenes

Un servidor de Protocolo de Contexto de Modelo (MCP) que permite la generación fluida de imágenes de alta calidad utilizando el modelo Flux.1 Schnell mediante Together AI. Este servidor proporciona una interfaz estandarizada para especificar los parámetros de generación de imágenes.

Pregúntale a DeepWiki

Características

  • Generación de imágenes de alta calidad impulsada por el modelo Flux.1 Schnell

  • Soporte para dimensiones personalizables (ancho y alto)

  • Manejo claro de errores para una rápida validación y problemas de API

  • Fácil integración con clientes compatibles con MCP

  • Guardado opcional de imagen en disco en formato PNG

Related MCP server: Image Generation MCP Server

Instalación

npm install together-mcp

O ejecutar directamente:

npx together-mcp@latest

Configuración

Agregue a su configuración de servidor MCP:

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

Uso

El servidor proporciona una herramienta: generate_image

Usando generate_image

Esta herramienta solo requiere un parámetro: el mensaje. Los demás parámetros son opcionales y, si no se proporcionan, se utilizan valores predeterminados razonables.

Parámetros

{
  // 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
}

Ejemplo de solicitud mínima

Solo se requiere el mensaje:

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

Ejemplo de solicitud completa con guardado de imagen

Anule los valores predeterminados y especifique una ruta para guardar la imagen:

{
  "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"
  }
}

Formato de respuesta

La respuesta será un objeto JSON que contendrá:

{
  "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")
    }
  ]
}

Si se proporcionó image_path y el guardado se realizó correctamente, la respuesta incluirá la confirmación de la ubicación de guardado.

Valores predeterminados

Si no se especifica en la solicitud, se utilizan estos valores predeterminados:

  • Modelo: "black-forest-labs/FLUX.1-schnell-Free"

  • ancho: 1024

  • altura: 768

  • pasos: 1

  • n: 1

  • formato_de_respuesta: "b64_json"

Notas importantes

  1. Solo se requiere el parámetro prompt

  2. Todos los parámetros opcionales utilizan valores predeterminados si no se proporcionan

  3. Cuando se proporcionan, los parámetros deben cumplir con sus restricciones (por ejemplo, rangos de ancho/alto)

  4. Las respuestas Base64 pueden ser grandes: use el formato URL para imágenes más grandes

  5. Al guardar imágenes, asegúrese de que el directorio especificado exista y se pueda escribir en él.

Prerrequisitos

Dependencias

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

Desarrollo

Clonar y construir el proyecto:

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

Scripts disponibles

  • npm run build - Construye el proyecto TypeScript

  • npm run watch : vigila los cambios y reconstruye

  • npm run inspector - Ejecutar el inspector MCP

Contribuyendo

¡Agradecemos sus contribuciones! Siga estos pasos:

  1. Bifurcar el repositorio

  2. Crear una nueva rama ( feature/my-new-feature )

  3. Confirme sus cambios

  4. Empuja la rama hacia tu tenedor

  5. Abrir una solicitud de extracción

Las solicitudes de funciones y los informes de errores se pueden enviar a través de GitHub Issues. Por favor, revise los problemas existentes antes de crear uno nuevo.

Para cambios significativos, primero abra un problema para discutir los cambios propuestos.

Licencia

Este proyecto está licenciado bajo la Licencia MIT. Consulte el archivo de LICENCIA para más detalles.

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

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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