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LinkedIn Jobs MCP Server

@Rom7699

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Rom7699

关于 LinkedIn Jobs MCP Server

A Model Context Protocol (MCP) server for searching and retrieving LinkedIn job postings via the RapidAPI LinkedIn Data API.

LinkedIn Jobs 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-jobs-mcp-server": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

LinkedIn Jobs MCP Server 工具

工具列表暂未提供

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 Jobs MCP Server?

A Model Context Protocol (MCP) server for searching and retrieving LinkedIn job postings via the RapidAPI LinkedIn Data API.

How to use LinkedIn Jobs MCP Server?

Install Python 3.8+, clone the repository, install required packages with pip install -r requirements.txt, create a .env file with your RAPIDAPI_KEY, and run python main.py to start the server. Then configure Claude Desktop by adding the server to claude_desktop_config.json.

Key features of LinkedIn Jobs MCP Server

  • Search for LinkedIn job postings using keywords
  • Filter jobs by location
  • Get detailed information about specific job postings
  • Location search functionality for finding LinkedIn location IDs

Use cases of LinkedIn Jobs MCP Server

  • Find software engineering jobs in a specific city with keyword search
  • Retrieve full details of a particular job listing by its ID
  • Look up LinkedIn location IDs to use in job searches
  • Integrate LinkedIn job data into AI-powered workflows via Claude Desktop

常见问题

1

What are the system requirements?

Python 3.8+ and a RapidAPI key with access to the LinkedIn Data API are required.

2

How do I configure the server with Claude Desktop?

Add the server to your `claude_desktop_config.json` specifying the Python command and path to the `main.py` file.

3

What tools does the server provide?

Three tools: `search_jobs(keywords, limit, location, format_output)`, `get_job_details(job_id)`, and `search_locations(keyword)`.

4

How do I install dependencies?

Run `pip install -r requirements.txt` from the project root directory.

5

Where does the job data come from?

All data is fetched from the LinkedIn Data API available through RapidAPI.

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