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Ayesha0300

LinkedIn Profile Scraper MCP Server

by Ayesha0300
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Servidor MCP para el raspador de perfiles de LinkedIn

Este servidor MCP utiliza la API Fresh LinkedIn Profile Data para obtener información del perfil de LinkedIn. Se implementa como un servidor de protocolo de contexto de modelo (MCP) y expone una única herramienta, get_profile , que acepta la URL de un perfil de LinkedIn y devuelve los datos del perfil en formato JSON.

Características

  • Obtener datos del perfil: recupera información del perfil de LinkedIn, incluidas las habilidades y otras configuraciones (con la mayoría de los detalles adicionales deshabilitados).

  • Solicitudes HTTP asincrónicas: utiliza httpx para llamadas API sin bloqueo.

  • Configuración basada en el entorno: lee RAPIDAPI_KEY de las variables de entorno mediante dotenv .

Related MCP server: linkedin-mcp-server

Prerrequisitos

  • Python 3.7+ – Asegúrese de estar utilizando la versión 3.7 de Python o superior.

  • Marco MCP: asegúrese de que el marco MCP esté instalado.

  • Bibliotecas necesarias: instale httpx , python-dotenv y otras dependencias.

  • RAPIDAPI_KEY: Obtenga una clave API de RapidAPI y agréguela a un archivo .env en el directorio de su proyecto (o configúrela en su entorno).

Instalación

  1. Clonar el repositorio:

    git clone https://github.com/AIAnytime/Awesome-MCP-Server
cd linkedin_profile_scraper

  2. Dependencias de instalación:

    uv add mcp[cli] httpx requests

  3. Configurar variables de entorno:

    Cree un archivo .env en el directorio del proyecto con el siguiente contenido:

    RAPIDAPI_KEY=your_rapidapi_key_here

Ejecución del servidor

Para ejecutar el servidor MCP, ejecute:

uv run linkedin.py

El servidor se iniciará y escuchará las solicitudes entrantes a través de E/S estándar.

Configuración del cliente MCP

Para conectar su cliente MCP a este servidor, agregue la siguiente configuración a su config.json . Ajuste las rutas según sea necesario para su entorno:

{
  "mcpServers": {
    "linkedin_profile_scraper": {
      "command": "C:/Users/aiany/.local/bin/uv",
      "args": [
        "--directory",
        "C:/Users/aiany/OneDrive/Desktop/YT Video/linkedin-mcp/project",
        "run",
        "linkedin.py"
      ]
    }
  }
}

Descripción general del código

  • Configuración del entorno: el servidor utiliza dotenv para cargar la RAPIDAPI_KEY necesaria para autenticarse con la API de datos nuevos del perfil de LinkedIn.

  • Llamada API: la función asincrónica get_linkedin_data realiza una solicitud GET a la API con parámetros de consulta especificados.

  • Herramienta MCP: la herramienta get_profile envuelve la llamada API y devuelve datos JSON formateados o un mensaje de error si la llamada falla.

  • Ejecución del servidor: el servidor MCP se ejecuta con el transporte stdio .

Solución de problemas

  • Falta RAPIDAPI_KEY: Si la clave no está configurada, el servidor generará un ValueError . Asegúrese de que la clave esté agregada a su archivo .env o configurada en su entorno.

  • Errores de API: si la solicitud de API falla, la herramienta devolverá un mensaje indicando que no se pudieron obtener los datos del perfil.

Licencia

Este proyecto está licenciado bajo la Licencia MIT. Consulte el archivo de LICENCIA para más detalles.

Available Tools

1 tool
get_profileB

Get LinkedIn profile data for a given profile URL.

Args:
    linkedin_url: The LinkedIn profile URL.
ParametersJSON Schema
NameRequiredDescriptionDefault
linkedin_urlYes

TDQS

B3.1/5.0
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 of behavioral disclosure. It mentions retrieving data but lacks details on permissions, rate limits, error handling, or output format. This is a significant gap for a tool with zero annotation coverage, though it at least correctly implies a read operation.

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 core purpose in the first sentence, followed by a structured Args section. It avoids redundancy and is appropriately sized for a single-parameter tool, though the formatting could be slightly more polished.

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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter but lacks details on behavioral traits and output, which are important for an agent to use it effectively without annotations.

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

Parameters4/5

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

With only one parameter and 0% schema description coverage, the description compensates by explaining the parameter's purpose ('The LinkedIn profile URL') in the Args section. This adds meaningful context beyond the bare schema, though it could specify format constraints (e.g., URL validation).

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 ('Get') and resource ('LinkedIn profile data'), and specifies the input requirement ('for a given profile URL'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

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, prerequisites, or contextual constraints. It only states what the tool does, not when it should be applied, leaving usage decisions entirely to the agent's inference.

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 observedget_profile
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

B3.2/5.0

Scored across 1 tool

Disambiguation5/5

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

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (get_profile), and with only one tool, consistency is inherently perfect as there are no other tools to compare against.

Tool Count2/5

A single tool is too few for a server labeled as a 'LinkedIn Profile Scraper MCP Server,' which suggests a broader scope. This minimal set feels thin and underdeveloped for scraping tasks that might include multiple operations like search, batch processing, or data extraction beyond single profiles.

Completeness2/5

The tool surface is severely incomplete for a LinkedIn scraper. It only allows fetching a single profile by URL, missing essential operations such as searching for profiles, handling authentication, pagination, or extracting additional data like connections or posts, which are typical in scraping workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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