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Image Generation MCP Server

by sarthakkimtani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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 mediante Together AI. Este servidor proporciona una interfaz estandarizada para especificar los parámetros de generación de imágenes.

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

Related MCP server: MCP Replicate FLUX

Instalación

Escritorio de Claude

  • En MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • En Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "image-gen": {
      "command": "uv",
      "args": ["--directory", "/ABSOLUTE/PATH/TO/image-gen/", "run", "image-gen"],
      "env": {
        "TOGETHER_AI_API_KEY": "<API KEY>"
      }
    }
  }
}

Herramientas disponibles

El servidor implementa una herramienta:

generar_imagen

Genera una imagen basada en el mensaje textual dado y dimensiones opcionales.

Esquema de entrada:

{
  "prompt": {
    "type": "string",
    "description": "A descriptive prompt for generating the image (e.g., 'a futuristic cityscape at sunset')"
  },
  "width": {
    "type": "integer",
    "description": "Width of the generated image in pixels (optional)"
  },
  "height": {
    "type": "integer",
    "description": "Height of the generated image in pixels (optional)"
  },
  "model": {
    "type": "string",
    "description": "The exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell)."
  }
}

Prerrequisitos

  • Python 3.12 o superior

  • httpx

  • mcp

Contribuyendo

¡Agradecemos sus contribuciones! Siga estos pasos para contribuir:

  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

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 based on the text prompt, model, and optional dimensions

ParametersJSON Schema
NameRequiredDescriptionDefault
heightNoOptional height for the image
modelYesThe exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell).
promptYesThe text prompt for image generation
widthNoOptional width for the image

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('Generate an image') but lacks details on permissions, rate limits, output format, or error handling. For a generative tool with zero annotation coverage, this is insufficient.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately concise and well-structured.

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 of image generation, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like response format, errors, or constraints, leaving significant gaps for the agent to infer usage.

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 parameters. The description adds minimal value by listing inputs ('text prompt, model, and optional dimensions') but doesn't provide additional context beyond what the schema already specifies, aligning with the baseline for high coverage.

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 tool's purpose: 'Generate an image based on the text prompt, model, and optional dimensions.' It specifies the verb ('Generate') and resource ('image'), and mentions key inputs. However, with no sibling tools, it doesn't need to differentiate from alternatives, so a 5 is not warranted.

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 constraints. It simply lists inputs without context about appropriate scenarios or limitations, leaving the agent with minimal usage direction.

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 updatev1.0.0
    • First observedgenerate_image
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'generate_image' follows a clear verb_noun pattern.

Tool Count2/5

A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, it lacks related operations like listing models, retrieving generated images, or managing parameters, making it borderline too few for the domain.

Completeness2/5

The server is severely incomplete for image generation. It only offers generation without any supporting tools for model selection, image retrieval, history management, or parameter tuning, which are common in such domains, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessNo issues

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