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Image Generation MCP Server

by mikeyny
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Servidor MCP de generación de imágenes

Una implementación de servidor MCP (Protocolo de contexto de modelo) para generar imágenes utilizando el modelo black-forest-labs/flux-schnell de Replicate.

Lo ideal es utilizarlo con la función MCP de Cursor, pero se puede utilizar con cualquier cliente MCP.

Características

  • Generar imágenes a partir de indicaciones de texto

  • Parámetros de imagen configurables (resolución, relación de aspecto, calidad)

  • Guardar las imágenes generadas en el directorio especificado

  • Cumplimiento total del protocolo MCP

  • Manejo de errores y validación

Related MCP server: replicate-flux-mcp

Prerrequisitos

  • Node.js 16+

  • Replicar token de API

  • SDK de TypeScript para MCP

Configuración

  1. Clonar el repositorio

  2. Instalar dependencias:

    npm install

  3. Agregue su token de API de replicación directamente en el código en src/imageService.ts actualizando la constante apiToken :

    // No environment variables are used since they can't be easily set in cursor
const apiToken = "your-replicate-api-token-here";

    Nota: Si lo usa con Claude, puede crear un archivo .env en el directorio raíz y configurar su token de API allí:

    REPLICATE_API_TOKEN=your-replicate-api-token-here

    Luego construye el proyecto:

    npm run build

Uso

Para utilizar con el cursor:

  1. Ir a Configuración

  2. Seleccionar funciones

  3. Desplácese hacia abajo hasta "Servidores MCP"

  4. Haga clic en "Agregar nuevo servidor MCP"

  5. Establezca el tipo en "Comando"

  6. Establezca el comando en: node ./path/to/dist/server.js

Parámetros de API

Parámetro

Tipo

Requerido

Por defecto

Descripción

prompt

cadena

Sí

-

Solicitud de texto para la generación de imágenes

output_dir

cadena

Sí

-

Ruta del directorio del servidor para guardar las imágenes generadas

go_fast

booleano

No

FALSO

Habilitar el modo de generación más rápido

megapixels

cadena

No

"1"

Calidad de resolución ("1", "2", "4")

num_outputs

número

No

1

Número de imágenes a generar (1-4)

aspect_ratio

cadena

No

"1:1"

Relación de aspecto ("1:1", "4:3", "16:9")

output_format

cadena

No

"webp"

Formato de imagen ("webp", "png", "jpeg")

output_quality

número

No

80

Calidad de compresión (1-100)

num_inference_steps

número

No

4

Número de pasos de eliminación de ruido (4-20)

Ejemplo de solicitud

{
  "prompt": "black forest gateau cake spelling out 'FLUX SCHNELL'",
  "output_dir": "/var/output/images",
  "filename": "black_forest_cake",
  "output_format": "webp"
  "go_fast": true,
  "megapixels": "1",
  "num_outputs": 2,
  "aspect_ratio": "1:1"
}

Ejemplo de respuesta

{
  "image_paths": [
    "/var/output/images/output_0.webp",
    "/var/output/images/output_1.webp"
  ],
  "metadata": {
    "model": "black-forest-labs/flux-schnell",
    "inference_time_ms": 2847
  }
}

Manejo de errores

El servidor maneja los siguientes tipos de errores:

  • Errores de validación (parámetros no válidos)

  • Errores de API (Replicar problemas de API)

  • Errores del servidor (sistema de archivos, permisos)

  • Errores desconocidos (problemas inesperados)

Cada respuesta de error incluye:

  • Código de error

  • Mensaje legible para humanos

  • Información detallada del error

Licencia

ISC

Available Tools

1 tool
generate-imageC

Generate an image based on a prompt

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYes
output_dirYesFull absolute path to output directory. For Windows, use double backslashes like 'C:\\Users\\name\\path'. For Unix/Mac use '/path/to/dir'. Always use the proper path otherwise you will get an error.
filenameNoBase filename to save the image(s) with
go_fastNo
megapixelsNo
num_outputsNo
aspect_ratioNo
output_formatNo
output_qualityNo
num_inference_stepsNo

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the core action (generating images from prompts) but lacks critical behavioral details like whether this is a local or cloud operation, rate limits, authentication requirements, error handling, or what happens when files are saved. The description doesn't contradict annotations since none exist.

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 at just 5 words, front-loaded with the core functionality. Every word earns its place, and there's no wasted text or 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?

For a complex tool with 10 parameters, no annotations, no output schema, and low schema description coverage, the description is severely inadequate. It doesn't explain what the tool returns, how outputs are structured, error conditions, or provide enough context for an agent to use it effectively beyond the most basic invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only 20% schema description coverage, the description must compensate but fails to do so. It mentions 'based on a prompt' which hints at one parameter but doesn't explain the other 9 parameters or their relationships. The description adds minimal value beyond what's already in the schema.

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 with a specific verb ('generate') and resource ('image'), and specifies it's based on a prompt. It's unambiguous about what the tool does, though it doesn't distinguish from siblings since there are none.

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, nor any context about prerequisites or limitations. It simply states what the tool does without indicating appropriate use cases.

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 update
    • 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 confusion or overlap between tools. The single tool 'generate-image' has a clear and distinct purpose for image generation based on prompts.

Naming Consistency5/5

The single tool name 'generate-image' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool is too few for a server named 'Image Generation MCP Server', which suggests a broader scope. While it covers basic generation, typical image generation domains might include variations, edits, or management tools, making this feel thin and incomplete.

Completeness2/5

The tool surface is severely incomplete for image generation. It only provides generation, with no tools for editing, resizing, filtering, or managing images, leaving obvious gaps that will limit agent capabilities in this domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

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