linkedin-mcp
Language:
Python
Stars:
9
Forks:
2
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
-
Profile Retrieval
Fetch user profiles using
get_profile()
function Extract key information such asname
,headline
, andcurrent position
-
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
- Feed Posts
- Retrieve LinkedIn feed posts using
get_feed_posts()
- Configurable limit and offset for pagination
- Resume Analysis
- Parse and extract information from
resumes (PDF format)
- Extracted data includes:
- Name
- Phone number
- Skills
- Work experience
- Education
- Languages
Configuration
After cloning the repo, adjust the `` accordingly
{
"linkedin":{
"command":"uv",
"args": [
"--directory",
"",
"run",
"linkedin.py"
]
}
}
Usage
I have been testing using MCP-client and found as the best one for testing your MCP-Servers
.
Publisher info
Hritik Raj
ECE undergrad at BITS Pilani Hyderabad|| Final Yearite || Data Science || LLMs ||Java,C++ - CC || Git || Web-Dev||
More MCP servers built with Python
Create, backtest, and execute trades directly in one chat box. The Composer MCP Server gives LLMs the power to backtest investment ideas and execute automated trading strategies. Trade across stocks, ETFs, and crypto directly in Claude.
An MCP to generate presentations with AI. Create and edit PowerPoint presentations with AI.
The PaddleOCR MCP server brings enterprise-grade OCR and document parsing capabilities to AI applications. Built on PaddleOCR — a proven solution with 50,000+ GitHub stars, deeply integrated by leading projects like MinerU, RAGFlow, and OmniParser— with targeted optimizations based on the MCP concept.