About MCP Server for LinkedIn
MCP Server for LinkedIn is an MCP server that uses an unofficial LinkedIn API to fetch profiles, search jobs, read feed posts and parse resumes. It suits users who want job-search tasks handled through AI conversations.
Key features
- 01Profile retrieval with name and headline
- 02Job search with multiple filters
- 03Feed posts with limit and offset
- 04PDF resume parsing
Use cases
- 01Searching for jobs by keyword, location and job type
- 02Extracting skills and work experience from a PDF resume
- 03Reviewing recent LinkedIn feed posts
MCP support
The project is an MCP server that exposes LinkedIn profile, job and feed queries as tools for MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP 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 <LOCAL_PATH> accordingly
{
"linkedin":{
"command":"uv",
"args": [
"--directory",
"<LOCAL_PATH>",
"run",
"linkedin.py"
]
}
}
Usage
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Comments
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