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LinkedIn Profile Scraper MCP Server

@codingaslu

代码仓库

基本信息

分类

浏览器自动化

传输方式

stdio

作者

codingaslu

关于 LinkedIn Profile Scraper MCP Server

This MCP server uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It is implemented as a model context protocol (MCP) server and exposes a single tool, get_profile, which accepts a LinkedIn profile URL and returns the profile data in JSON format.

#linkedin#profile-scraper#data-fetcher

LinkedIn Profile Scraper MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Linkedin_Mcp_Server": {
      "command": "uv",
      "args": [
        "run",
        "linkedin.py"
      ]
    }
  }
}

LinkedIn Profile Scraper MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is LinkedIn Profile Scraper MCP Server?

It is an MCP server that uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It exposes a single tool, get_profile, which accepts a LinkedIn profile URL and returns the profile data in JSON format.

How to use LinkedIn Profile Scraper MCP Server?

Install dependencies with uv add mcp[cli] httpx requests. Create a .env file with your RAPIDAPI_KEY. Run the server using uv run linkedin.py. Then configure your MCP client to point to the server, specifying the uv command, the project directory, and the script as arguments.

Key features of LinkedIn Profile Scraper MCP Server

  • Fetches LinkedIn profile data via RapidAPI
  • Uses asynchronous HTTP requests (httpx)
  • Environment-based configuration (dotenv)
  • Exposes a single get_profile tool
  • Returns profile data in JSON format

Use cases of LinkedIn Profile Scraper MCP Server

  • Retrieve LinkedIn profile information for data enrichment
  • Automate background checks or candidate screening
  • Integrate profile data into AI-powered workflows
  • Enrich CRM records with external professional details

常见问题

1

What API does the server use?

It uses the Fresh LinkedIn Profile Data API available through RapidAPI.

2

What are the prerequisites to run the server?

You need Python 3.7+, the MCP framework, installed dependencies (`httpx`, `python-dotenv`, etc.), and a valid `RAPIDAPI_KEY`.

3

How do I obtain an API key?

Obtain an API key from [RapidAPI](https://rapidapi.com/) and add it to a `.env` file or set it in your environment.

4

What happens if the `RAPIDAPI_KEY` is missing?

The server will raise a `ValueError` indicating the key is missing.

5

What occurs if the API request fails?

The `get_profile` tool returns a message indicating that the profile data could not be fetched.

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