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MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster - MCP Implementation

MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster

Tired of LinkedIn job apps that drag? 🚀 MCP Server turns your hunt into hyperdrive—apply faster, stand out more, and land offers before ‘maybe’ turns to ‘nope’!

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Users create an average of 21 projects per month with this tool

About MCP Server for LinkedIn

What is MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster?

Designed as a streamlined integration layer, the MCP Server harnesses unofficial LinkedIn API endpoints to amplify job search efficacy. By automating profile parsing, feed navigation, and resume analysis, this tool empowers professionals to navigate LinkedIn's ecosystem with precision—transforming scattered workflows into actionable insights. Its architecture prioritizes developer-friendly workflows while maintaining compliance with API constraints.

How to use MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster?

Initialization demands adjusting the linkedin.py configuration with your local directory path. Authentication occurs via client credentials, after which developers can invoke methods like get_profile() or get_feed_posts() through the MCP protocol. For interactive testing, I highly recommend pairing with the MCP-client—its CLI interface provides immediate feedback during API endpoint validation.

MCP Server for LinkedIn Features

Key Features of MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster?

  • Context-Aware Profiling: Extracts professional metadata (name, headline, role) with 98% accuracy using semantic parsing
  • Granular Job Search: Filters opportunities via 8+ parameters including remote work options and skill proficiencies
  • Feed Analytics: Paginated post retrieval with offset management for trend analysis
  • Resume Intelligence: Structured PDF parsing delivering standardized candidate data models

Use cases of MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster?

This server excels in scenarios requiring programmatic LinkedIn engagement:

  • Recruitment bots analyzing 100+ resumes daily
  • Career coaches benchmarking industry trends via feed sentiment analysis
  • Startup teams automating job postings to multiple platforms

MCP Server for LinkedIn FAQ

FAQ from MCP Server for LinkedIn: Hyperdrive Applications, Land Offers Faster?

Q: Does this support multi-account authentication?
A: Yes, credential arrays can be configured in the args section for parallel processing.

Q: What’s the maximum search limit?
A: Adjustable up to API-imposed caps, currently 1000 results per request with pagination support.

Q: Are parsed resumes GDPR-compliant?
A: The server includes anonymization options, but implementation depends on client-side processing.

Content

MCP Server for LinkedIn

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

  1. 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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