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GMKR

mcp-image-generator

by GMKR
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Generador de imágenes MCP

Un servidor de Protocolo de Contexto de Modelo (MCP) para generar imágenes utilizando los modelos de generación de imágenes de Together AI. Este servidor MCP puede ejecutarse localmente o mediante un punto final de SSE. El generador de imágenes MCP requiere un proveedor; actualmente solo se admiten "Replicar" y "Juntos". Debe configurar las variables de entorno TOGETHER_API_KEY o REPLICATE_API_TOKEN y la variable de entorno PROVIDER en "replicar" o "juntos".

Punto final de SSE (entorno Docker)

Clonar el repositorio

git clone https://github.com/gmkr/mcp-imagegen.git
cd mcp-imagegen

Construir y ejecutar un contenedor Docker

docker build -f Dockerfile.server -t mcp-imagegen .
docker run -p 3000:3000 mcp-imagegen

Configuración con el cliente MCP

{
  "mcpServers": {
    "imagegenerator": {
      "url": "http://localhost:3000/sse",
      "env": {
        "PROVIDER": "replicate",
        "REPLICATE_API_TOKEN": "your-replicate-api-token"
      }
    }
  }
}

Ajuste la url al punto final del servidor MCP que desea utilizar. provider puede ser "replicar" o "juntos".

Related MCP server: pixel-surgeon-mcp

Ejecutándose localmente usando stdio

Prerrequisitos

  • Node.js

  • Clave API de inteligencia artificial o token de API de replicación

Instalación

  1. Clonar el repositorio:

    git clone https://github.com/gmkr/mcp-imagegen.git
cd mcp-imagegen

  2. Instalar dependencias:

    pnpm install

Configuración

Cree un archivo de configuración para su cliente MCP. A continuación, se muestra un ejemplo de configuración:

{
  "mcpServers": {
    "imagegenerator": {
      "command": "pnpx",
      "args": [
        "-y",
        "tsx",
        "/path/to/mcp-imagegen/src/index.ts"
      ],
      "env": {
        "PROVIDER": "replicate",
        "REPLICATE_API_TOKEN": "your-replicate-api-token"
      }
    }
  }
}

Reemplace /path/to/mcp-imagegen con la ruta absoluta a su repositorio clonado y your-replicate-api-token con su token de API de replicación real.

Uso

El generador de imágenes MCP proporciona una herramienta llamada generate_image que puede utilizarse para generar imágenes basadas en indicaciones de texto.

Herramienta: generate_image

Genera una imagen según el mensaje proporcionado.

Parámetros:

  • prompt (cadena): El texto que solicita que se genere una imagen para

  • width (número, opcional): el ancho de la imagen a generar (predeterminado: 512)

  • height (número, opcional): La altura de la imagen a generar (predeterminado: 512)

  • numberOfImages (número, opcional): El número de imágenes a generar (predeterminado: 1)

Variables de entorno

  • PROVIDER : El proveedor que se utilizará para la generación de imágenes (predeterminado: "replicar")

  • REPLICATE_API_TOKEN : Su token de API de replicación

  • TOGETHER_API_KEY : Su clave API de Together AI

  • MODEL_NAME : El modelo que se utilizará para la generación de imágenes (predeterminado: "black-forest-labs/flux-schnell")

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Available Tools

1 tool
generate_imageA

Generates and returns an image based on the provided promptUse this tool when you need to generate an image based on a promptThe image will be returned as a base64 encoded string

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt to generate an image for
widthNoThe width of the image to generate
heightNoThe height of the image to generate
numberOfImagesNoThe number of images to generate

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the image is 'returned as a base64 encoded string,' which adds useful behavioral context beyond the input schema. However, it lacks details on potential limitations (e.g., rate limits, quality constraints, or error conditions), leaving gaps for a mutation tool.

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 front-loaded with the purpose and usage guidelines in two sentences, with no wasted words. However, the lack of punctuation between sentences ('promptUse this tool') slightly reduces readability, preventing a perfect score of 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (image generation with 4 parameters) and no annotations or output schema, the description is moderately complete. It covers the basic operation and output format but lacks details on behavioral traits (e.g., performance, errors) and does not explain return values beyond the base64 string, leaving room for improvement.

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?

The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description does not add any parameter-specific details beyond what the schema provides (e.g., format or constraints for 'prompt' or 'width'). Thus, it meets the baseline of 3 but does not enhance parameter understanding.

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: 'Generates and returns an image based on the provided prompt.' It specifies both the action (generate and return) and the resource (image). However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, which prevents a score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes explicit guidance: 'Use this tool when you need to generate an image based on a prompt.' This clearly indicates the primary use case. However, it lacks exclusions or alternatives (e.g., when not to use it or other tools for similar tasks), which prevents a score of 5.

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. Dates show when Glama detected each change.

  1. 1 tool update
    • First observedgenerate_image
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TDQS

A3.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clear and distinct purpose that cannot be confused with any other tool in this set.

Naming Consistency5/5

The single tool name 'generate_image' follows a clear verb_noun pattern. Since there is only one tool, naming 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 'mcp-image-generator', which suggests a broader scope for image generation tasks. While the tool covers basic generation, the count feels thin and lacks operations like editing, upscaling, or managing generated images that might be expected.

Completeness2/5

The tool set is severely incomplete for an image generation domain. It only provides generation, with no coverage for common operations like editing images, adjusting parameters, retrieving generation history, or handling different formats, which will limit agent capabilities and cause workarounds.

Maintenance

ActivityInactive
ResponsivenessSyncing

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