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GongRzhe

Image Generation MCP Server

by GongRzhe
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JavaScript
Remote

Servidor MCP de generación de imágenes

Este servidor MCP proporciona capacidades de generación de imágenes utilizando el modelo Replicate Flux.

Instalación

Opción 1: Método NPX (no requiere configuración local)

Puede utilizar el paquete directamente desde npm sin instalarlo localmente:

# No installation needed - npx will handle it

Opción 2: Instalación local

Si prefiere una instalación local:

# Global installation
npm install -g @gongrzhe/image-gen-server

# Or local installation
npm install @gongrzhe/image-gen-server

Related MCP server: FLUX MCP Server

Configuración

Configurar Claude Desktop

Edite su archivo de configuración de Claude Desktop:

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

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

Opción 1: Configuración NPX (recomendada)

Este método ejecuta el servidor directamente desde npm sin necesidad de archivos locales:

{
  "mcpServers": {
    "image-gen": {
      "command": "npx",
      "args": ["@gongrzhe/image-gen-server"],
      "env": {
        "REPLICATE_API_TOKEN": "your-replicate-api-token",
        "MODEL": "alternative-model-name"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Opción 2: Configuración de instalación local

Si instaló el paquete localmente:

{
  "mcpServers": {
    "image-gen": {
      "command": "node",
      "args": ["/path/to/image-gen-server/build/index.js"],
      "env": {
        "REPLICATE_API_TOKEN": "your-replicate-api-token",
        "MODEL": "alternative-model-name"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Obtenga su token de API de réplica

  1. Regístrese/inicie sesión en https://replicate.com

  2. Vaya a https://replicate.com/account/api-tokens

  3. Crear un nuevo token de API

  4. Copie el token y reemplace your-replicate-api-token en la configuración de MCP

imagen

Variables de entorno

  • REPLICATE_API_TOKEN (obligatorio): Su token de API de replicación para autenticación

  • MODEL (opcional): El modelo de réplica que se usará para la generación de imágenes. El valor predeterminado es "black-forest-labs/flux-schnell".

Parámetros de configuración

  • disabled : controla si el servidor está habilitado ( false ) o deshabilitado ( true )

  • autoApprove : Matriz de nombres de herramientas que se pueden ejecutar sin confirmación del usuario. Una matriz vacía significa que todas las llamadas a herramientas requieren confirmación.

Herramientas disponibles

generar_imagen

Genera imágenes utilizando el modelo Flux basándose en indicaciones de texto.

imagen

fuera-0 (1)

Parámetros

  • prompt (obligatorio): Descripción de texto de la imagen a generar

  • seed (opcional): semilla aleatoria para generación reproducible

  • aspect_ratio (opcional): Relación de aspecto de la imagen (predeterminado: "1:1")

  • output_format (opcional): Formato de salida: "webp", "jpg" o "png" (predeterminado: "webp")

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

Ejemplo de uso

const result = await use_mcp_tool({
  server_name: "image-gen",
  tool_name: "generate_image",
  arguments: {
    prompt: "A beautiful sunset over mountains",
    aspect_ratio: "16:9",
    output_format: "png",
    num_outputs: 1
  }
});

La herramienta devuelve una matriz de URL a las imágenes generadas.

📜 Licencia

Este proyecto está licenciado bajo la licencia MIT.

Available Tools

1 tool
generate_imageC

Generate an image using the Flux model

NameRequiredDescriptionDefault
promptYesPrompt for generated image
seedNoRandom seed for reproducible generation
aspect_ratioNoAspect ratio for the generated image1:1
output_formatNoFormat of the output imageswebp
num_outputsNoNumber of outputs to generate (1-4)

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 states the action ('generate') but doesn't mention cost, rate limits, permissions, or output behavior (e.g., file format, size). This is a significant gap for a generative tool with potential resource implications.

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 with zero waste. It's front-loaded with the core action and model, making it easy to parse quickly. Every word earns its place without redundancy.

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, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like cost, rate limits, or output details (e.g., image size, quality), leaving gaps for the agent to operate effectively.

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 no additional meaning beyond the schema, such as explaining prompt best practices or seed usage. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('generate') and resource ('image'), specifying the model ('Flux model'). It's specific about what the tool does, though without sibling tools, differentiation isn't applicable. It's not tautological or misleading.

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 this tool versus alternatives, prerequisites, or constraints. The description lacks context for usage, such as when image generation is appropriate or any limitations, leaving the agent without operational guidance.

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 updatev1.0.1
    • 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

B3/5.0

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 of generating images using the Flux model.

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 cause inconsistency.

Tool Count2/5

A single tool for an image generation server is too few for the apparent scope, as it lacks basic operations like listing models, managing generations, or handling variations. This minimal set will likely cause agent failures due to incomplete functionality.

Completeness1/5

The tool surface is severely incomplete for an image generation domain. It only provides generation without any supporting operations such as model selection, parameter tuning, history retrieval, or image editing, leading to dead ends in agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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