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MCP Image Placeholder Server

by husniadil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Remote

Servidor de marcador de posición de imagen MCP

Este es un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona una herramienta para generar imágenes de marcador de posición de diferentes proveedores.

Características

  • Genera imágenes de marcador de posición de proveedores compatibles

  • Admite dos proveedores de imágenes:

    • placehold : proporciona imágenes de marcador de posición simples

    • lorem-picsum : proporciona imágenes reales como imágenes de marcador de posición

  • Valida los parámetros de entrada

  • Devuelve URL de imágenes para uso inmediato

Related MCP server: Grok AI Image Generation MCP Server

Requisitos

  • Python 3.9+

  • administrador de paquetes uv

Instalación

  1. Clonar este repositorio

  2. Configurar la configuración para el servidor MCP

Uso

El servidor expone una herramienta:

image_placeholder

Generar una URL de imagen de marcador de posición basada en parámetros especificados.

Parámetros:

  • provider : El proveedor de imágenes a utilizar ( placehold o lorem-picsum )

  • width : El ancho de la imagen (1-10000)

  • height : La altura de la imagen (1-10000)

Devoluciones:

  • Cadena URL de la imagen generada

Ejemplo de uso:

# Generate a 300x200 placeholder image
url = image_placeholder(provider="placehold", width=300, height=200)

# Generate a 500px square lorem-picsum image
url = image_placeholder(provider="lorem-picsum", width=500)

Configuración

Para conectar este servidor a Claude for Desktop:

  1. Agregue lo siguiente a su claude_desktop_config.json :

    {
    "mcpServers": {
        "image-placeholder": {
            "command": "uv",
            "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/PROJECT",
                "run",
                "main.py"
            ]
        }
    }
}

  2. Reiniciar Claude para escritorio

Para conectar este servidor a Cursor:

  1. Abrir configuración del cursor

  2. Dirígete a la sección Features

  3. Desplácese hacia abajo hasta la sección MCP Servers

  4. Haga clic en el botón Add new MCP server

  5. Introduzca la siguiente información:

    • Nombre: image-placeholder

    • Tipo: command

    • URL del servidor: uv --directory /ABSOLUTE/PATH/TO/PROJECT run main.py

  6. Haga clic en el botón Add ↵

Solución de problemas

Si no se detecta la herramienta, utilice la ruta absoluta del comando uv , por ejemplo

/ABSOLUTE/PATH/TO/uv --directory /ABSOLUTE/PATH/TO/PROJECT run main.py

Ejemplo de uso y salida (cursor)

Inmediato:

Create a new directory named "example" and a file named output.html.

Then create a single modern looking page using tailwindcss: https://unpkg.com/@tailwindcss/browser@4

Show a nice header, content, and footer, showing a photo gallery.

Save this into output.html

Captura de pantalla de Cursor Agent

Salida: Ejemplo de salida (cursor)

Licencia

Licencia MIT

Available Tools

1 tool
image_placeholderA
Generate a placeholder image based on a provider, width, and height.
Use this tool to generate a placeholder image for testing or development purposes.

Args:
    provider: The provider to use for the image, must be either `placehold` or `lorem-picsum`.
    width: The width of the image, must be a positive integer between 1 and 10000.
    height: The height of the image, must be a positive integer between 1 and 10000.
ParametersJSON Schema
NameRequiredDescriptionDefault
providerYes
widthYes
heightYes

TDQS

A3.9/5.0
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. It mentions the tool generates an image but lacks details on behavioral traits like output format (e.g., URL, binary data), error handling, rate limits, or authentication needs. This leaves gaps for an agent to understand how to use it effectively.

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 appropriately sized and front-loaded, with the purpose stated first and parameter details organized in a clear 'Args' section. It avoids unnecessary fluff, though the second sentence slightly repeats the purpose without adding new value.

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 no annotations and no output schema, the description is incomplete for a tool that generates output. It explains parameters well but omits details on what the tool returns (e.g., image URL or data), which is critical for an agent to use it correctly. This gap reduces completeness despite good parameter coverage.

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

Parameters5/5

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

The schema description coverage is 0%, but the description compensates fully by explaining all three parameters in the 'Args' section: provider options, width/height ranges, and constraints. This adds crucial meaning beyond the bare schema, making parameters clear and actionable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Generate a placeholder image') and resource ('based on a provider, width, and height'), with the second sentence reinforcing the purpose for testing/development. It uses precise verbs and distinguishes the tool's function without tautology.

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 provides clear context for when to use the tool ('for testing or development purposes'), which helps guide the agent. However, since there are no sibling tools mentioned, it cannot differentiate from alternatives, though this is not a flaw given the context.

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 observedimage_placeholder
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

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

Naming Consistency5/5

The single tool name 'image_placeholder' follows a clear and consistent verb_noun pattern. Since there is only one tool, naming consistency is inherently perfect.

Tool Count2/5

A single tool for an image placeholder server feels thin and limited in scope. While it serves a specific purpose, it lacks related operations like listing available providers or generating images with additional parameters, making it borderline too few for practical use.

Completeness3/5

The tool covers the core functionality of generating placeholder images with basic parameters. However, there are notable gaps, such as no tools for retrieving image metadata, managing providers, or handling errors beyond parameter validation, which limits the server's utility.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

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