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Letz-AI

Letz AI MCP

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Guía de configuración de LetzAI MCP

Esta guía lo guiará a través del proceso de configuración y uso del MCP (Protocolo de contexto de modelo) LetzAI para la generación de imágenes.

Prerrequisitos

Antes de comenzar, asegúrese de tener lo siguiente:

Related MCP server: iRAG MCP Server

Pasos de configuración

1. Descargue la carpeta Git

Descarga el repositorio que contiene el proyecto LetzAI MCP y guárdalo fuera de tu carpeta de Descargas. Por ejemplo:

C:\\Users\\username\\desktop

Alternativamente, puedes usar git clone para clonar el repositorio:

git clone <repository-url> C:\\Users\\username\\desktop

2. Instalar dependencias

Navegue a la carpeta del proyecto usando su terminal o símbolo del sistema:

cd C:\\Users\\username\\desktop

Ejecute el siguiente comando para instalar todas las dependencias necesarias:

npm install

3. Compilar el proyecto

Después de instalar las dependencias, compile los archivos TypeScript en JavaScript usando el siguiente comando:

npx tsc

Esto generará los archivos JavaScript compilados en la carpeta build .

4. Reinicia la aplicación Claude

Después de ejecutar npx tsc , debe reiniciar la aplicación de escritorio Claude para que reconozca la configuración de MCP actualizada y los archivos compilados.

5. Configure la configuración de MCP en la aplicación de escritorio Claude

abrir configuración

  1. Abra la aplicación de escritorio Claude .

  2. Haga clic en el ícono de Menú en la esquina superior izquierda.

  3. En el menú desplegable, seleccione Archivo .

  4. Vaya a Configuración .

  5. En la sección Desarrollador , verás una opción para Editar configuración .ver la configuración del desarrollador

  6. Haga clic en Editar configuración : esto abrirá la carpeta de configuración.

  7. Localice el archivo claude_desktop_config.json y edítelo según sea necesario.

Configuración de Windows:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": [
        "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\letzai-mcp\\build\\index.js"
      ],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

Configuración de Ubuntu:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/letzai-mcp/build/index.js"],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

Configuración de macOS:

{
  "mcpServers": {
    "letzai": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/letzai-mcp/build/index.js"],
      "env": {
        "LETZAI_API_KEY": "<Your LetzAI API Key>"
      }
    }
  }
}

Explicación de la configuración

  • Comando : El comando para ejecutar la aplicación. Usamos node para ejecutar el archivo JavaScript generado por TypeScript.

  • args : Esta es la ruta al archivo index.js compilado. Asegúrate de que la ruta sea correcta según la ubicación de tus archivos después de la compilación. Si has colocado la carpeta en C:\\Users\\username\\desktop\\letzai-mcp , la ruta será:


C:\\Users\\username\\desktop\\letzai-mcp\\build\\index.js

6. Ejecute el servidor MCP

Ahora que todo está configurado, puede empezar a usar el MCP de LetzAI en la aplicación de escritorio de Claude. El servidor debería estar listo para las tareas de generación de imágenes una vez que la aplicación se ejecute con la clave API correcta en el entorno.

Importante: Después de realizar cambios en la configuración, debe reiniciar Claude para que los cambios surtan efecto.

7. Prueba del nuevo MCP en Claude

Interfaz de usuario de Claude Prompt después de la instalación Haga clic en el icono del martillo para ver las herramientas MCP instaladas.herramientas de Claude MCP

Una vez que haya configurado el MCP en la aplicación de escritorio Claude, puede probarlo ejecutando el siguiente mensaje:

  • Crea una imagen con LetzAI usando el mensaje: "Foto de @mischstrotz bebiendo una cerveza, vestido de caballero".

Esto creará la imagen según la instrucción proporcionada, usando el modelo @mischstrotz de LetzAI. Claude abrirá la imagen en su navegador preferido.

Esto ampliará la imagen utilizando el parámetro de fuerza 1. Puede pasar URL completas o solo los ID de imagen de LetzAI, por ejemplo, d6a67077-f156-46d7-a1a2-1dc49e83dd91

Solución de problemas

  • No se encontró Node.js : asegúrese de que Node.js esté instalado y agregado a la variable de entorno PATH de su sistema.

  • Clave API no válida : verifique que haya agregado correctamente su clave API en la variable LETZAI_API_KEY en la configuración de la aplicación de escritorio Claude.

  • Problemas con la ruta de archivo : Asegúrese de que la ruta al archivo index.js sea correcta. Si no está seguro de la ruta, utilice la ruta absoluta del archivo.

Para obtener documentación y soporte más detallados, visita LetzAI Docs .

Available Tools

2 tools
letzai_create_imageC

Create an image using the LetzAI public api

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesImage prompt to generate a new image. Can also include @tag to generate an image using a model from the LetzAi Platform
widthNoWidth of the image should be between 520 and 2160 max pixels. Default is 1600.
heightNoHeight of the image should be between 520 and 2160 max pixels. Default is 1600.
qualityNoDefines how many steps the generation should take. Higher is slower, but generally better quality. Min: 1, Default: 2, Max: 5
creativityNoDefines how strictly the prompt should be respected. Higher Creativity makes the images more artificial. Lower makes it more photorealistic. Min: 1, Default: 2, Max: 5
hasWatermarkNoDefines whether to set a watermark or not. Default is true
systemVersionNoAllowed values: 2, 3. UseLetzAI V2, or V3 (newest).
modeNoSelect one of the different modes that offer different generation settings. Allowed values: default, sigma, turbo. Default is slow but high quality. Sigma is faster and great for close ups. Turbo is fastest, but lower quality.turbo

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 but only states the basic action. It doesn't cover authentication needs, rate limits, response format, error handling, or any side effects (e.g., whether creation is idempotent or has costs). This leaves significant gaps for an AI agent to understand operational behavior.

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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse while avoiding redundancy or fluff.

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 an 8-parameter image generation tool with no annotations and no output schema, the description is insufficient. It lacks details on return values, error conditions, usage constraints, and how it integrates with the sibling tool, leaving the agent with incomplete operational 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?

The schema description coverage is 100%, providing detailed documentation for all 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline score of 3 without compensating or enhancing 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 action ('create an image') and the target resource ('using the LetzAI public api'), making the purpose immediately understandable. It distinguishes from the sibling tool 'letzai_upscale_image' by focusing on generation rather than enhancement, though it doesn't explicitly contrast them.

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 or any contextual prerequisites. It mentions the LetzAI public API but doesn't specify use cases, limitations, or when to choose this over other image generation tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

letzai_upscale_imageC

Upscale an image using the LetzAI public api

ParametersJSON Schema
NameRequiredDescriptionDefault
imageIdNoThe unique identifier of the image to be upscaled.
imageUrlNoThe URL of the image to be upscaled. Must be a publicly available URL.
strengthYesThe strength of the upscaling process. Min. 1, Max. 3.

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 full burden for behavioral disclosure. It mentions using a public API but doesn't disclose critical traits like authentication requirements, rate limits, cost implications, error handling, or what happens to the original image. For a tool that modifies content with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 states the core purpose without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the essential information. Every word earns its place, making it maximally concise.

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 tool modifies images (implied mutation), has no annotations, and no output schema, the description is incomplete. It doesn't explain what 'upscale' means practically, what format/resolution results are expected, whether the operation is reversible, or what happens if both imageId and imageUrl are provided. For a 3-parameter tool with no structured safety or output information, more context is needed.

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 already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema. It doesn't explain the relationship between imageId and imageUrl, or provide context about strength values. This meets the baseline for high schema 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 action ('Upscale') and resource ('an image') using the LetzAI public API. It distinguishes from the sibling tool 'letzai_create_image' by focusing on upscaling existing images rather than creating new ones. However, it doesn't specify the exact upscaling method or output characteristics, keeping it at a 4 rather than a 5.

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. It doesn't mention prerequisites, limitations, or comparison with the sibling 'letzai_create_image' tool. The agent must infer usage from the tool name and parameters alone, which is insufficient for clear decision-making.

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. 2 tool updates
    • First observedletzai_create_image
    • First observedletzai_upscale_image
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TDQS

B3.1/5.0
Disambiguation5/5

The two tools have completely distinct purposes: one creates images from scratch, while the other upscales existing images. There is no overlap in functionality, and an agent can easily differentiate between them based on their clear descriptions.

Naming Consistency5/5

Both tools follow a consistent 'letzai_verb_noun' pattern with snake_case, using 'create_image' and 'upscale_image' as the core naming structure. This makes them predictable and easy to parse for an agent.

Tool Count2/5

With only two tools, the server feels thin for an AI image generation domain. While create and upscale are core operations, notable gaps like editing, inpainting, or style transfer are missing, making the toolset under-scoped for typical image manipulation workflows.

Completeness2/5

The server covers basic image creation and upscaling but lacks essential operations for a complete image generation surface. There are no tools for editing, modifying, or deleting images, and advanced features like batch processing or style application are absent, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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