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Ayesha0300

LinkedIn Profile Scraper MCP Server

by Ayesha0300
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

LinkedIn 个人资料抓取器 MCP 服务器

此 MCP 服务器使用 Fresh LinkedIn Profile Data API 来获取 LinkedIn 个人资料信息。它以模型上下文协议 (MCP) 服务器的形式实现，并公开了一个工具get_profile ，该工具接受 LinkedIn 个人资料 URL 并以 JSON 格式返回个人资料数据。

特征

  • **获取个人资料数据：**检索 LinkedIn 个人资料信息，包括技能和其他设置（大多数附加详细信息已禁用）。

  • **异步 HTTP 请求：**使用httpx进行非阻塞 API 调用。

  • **基于环境的配置：**使用dotenv从环境变量中读取RAPIDAPI_KEY 。

Related MCP server: linkedin-mcp-server

先决条件

  • Python 3.7+ – 确保您使用的是 Python 3.7 或更高版本。

  • **MCP 框架：**确保已安装 MCP 框架。

  • **所需库：**安装httpx 、 python-dotenv和其他依赖项。

  • **RAPIDAPI_KEY：**从RapidAPI获取 API 密钥并将其添加到项目目录中的.env文件中（或在您的环境中进行设置）。

安装

  1. 克隆存储库：

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

  2. 安装依赖项：

    uv add mcp[cli] httpx requests

  3. 设置环境变量：

    在项目目录中创建一个.env文件，其内容如下：

    RAPIDAPI_KEY=your_rapidapi_key_here

运行服务器

要运行 MCP 服务器，请执行：

uv run linkedin.py

服务器将启动并通过标准 I/O 监听传入的请求。

MCP 客户端配置

要将您的 MCP 客户端连接到此服务器，请将以下配置添加到您的config.json中。请根据您的环境调整路径：

{
  "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"
      ]
    }
  }
}

代码概述

  • **环境设置：**服务器使用dotenv加载使用 Fresh LinkedIn Profile Data API 进行身份验证所需的RAPIDAPI_KEY 。

  • **API 调用：**异步函数get_linkedin_data使用指定的查询参数向 API 发出 GET 请求。

  • MCP 工具： get_profile工具包装 API 调用并返回格式化的 JSON 数据，如果调用失败则返回错误消息。

  • 服务器执行： MCP 服务器通过stdio传输运行。

故障排除

  • **缺少 RAPIDAPI_KEY：**如果未设置该密钥，服务器将引发ValueError 。请确保已将该密钥添加到您的.env文件或在您的环境中进行设置。

  • **API 错误：**如果 API 请求失败，该工具将返回一条消息，表明无法获取配置文件数据。

执照

本项目遵循 MIT 许可证。更多详情请参阅许可证文件。

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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