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linkedin-mcp

一种模型上下文协议服务器,通过自然语言命令实现与LinkedIn的无缝交互,用于求职申请、资料检索、动态浏览和简历分析。

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README

MCP Server for LinkedIn

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A Model Context Protocol (MCP) server for linkedin to apply Jobs and search through feed seamlessly.

This uses Unoffical Linkedin API Docs for hitting at the clients Credentials.

Features

  1. Profile Retrieval

    Fetch user profiles using get_profile() function Extract key information such as name, headline, and current position

  2. Job Search

  • Advanced job search functionality with multiple parameters:
    • Keywords
    • Location
    • Experience level
    • Job type (Full-time, Contract, Part-time)
    • Remote work options
    • Date posted
    • Required skills
  • Customizable search limit
  1. Feed Posts
  • Retrieve LinkedIn feed posts using get_feed_posts()
  • Configurable limit and offset for pagination
  1. Resume Analysis
  • Parse and extract information from resumes (PDF format)
  • Extracted data includes:
    • Name
    • Email
    • Phone number
    • Skills
    • Work experience
    • Education
    • Languages

Configuration

After cloning the repo, adjust the <LOCAL_PATH> accordingly

{
    "linkedin":{
        "command":"uv",
        "args": [
            "--directory",
            "<LOCAL_PATH>",
            "run",
            "linkedin.py"
        ]
    }   
}     


Usage

I have been testing using MCP-client and found as the best one for testing your MCP-Servers.

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Runtime guide

cloud

Hosted runtime

Hosted servers run from a provider-managed environment. You usually connect the MCP client to the hosted endpoint or follow the provider's authorization flow, without keeping a local process alive

  1. Open provider connection page
  2. Authorize or copy endpoint
  3. Connect from your MCP client
terminal

Local runtime / other methods

Local servers run on your own machine or infrastructure. You normally copy the server_config into your MCP client, install the required package, and provide env variables from env_schema when needed

  1. Copy server_config
  2. Install required package
  3. Fill env variables and restart client