LinkedIn MCP server

This MCP server provides LinkedIn integration capabilities, allowing you to search jobs, retrieve profiles, analyze resumes, and access feed posts through the Model Context Protocol framework. It leverages an unofficial LinkedIn API to interact with LinkedIn using client credentials.

Installation

To install and configure the MCP server for LinkedIn:

1. Clone the repository to your local machine
2. Configure the server settings by updating the configuration file with your local path

Configuration

Adjust the configuration file by replacing <LOCAL_PATH> with the actual path where you've cloned the repository:

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

Usage

The MCP server for LinkedIn offers several core features that you can leverage through API calls.

Testing the Server

For testing the MCP server, you can use MCP-client, which is recommended for testing MCP servers.

Key Features

Profile Retrieval

Retrieve LinkedIn user profiles to extract key information:

# Example of retrieving a profile
profile = get_profile("username")

# Available profile information
name = profile.name
headline = profile.headline
current_position = profile.current_position

Job Search

Search for jobs on LinkedIn with multiple filtering options:

# Example job search with parameters
jobs = search_jobs(
    keywords="software engineer",
    location="San Francisco",
    experience_level="Mid-Senior level",
    job_type="Full-time",
    remote=True,
    date_posted="Past Week",
    required_skills=["Python", "JavaScript"],
    limit=10
)

Available job search parameters:

• Keywords
• Location
• Experience level
• Job type (Full-time, Contract, Part-time)
• Remote work options
• Date posted
• Required skills
• Result limit

Feed Posts

Retrieve posts from your LinkedIn feed:

# Get feed posts with pagination
posts = get_feed_posts(limit=20, offset=0)

Resume Analysis

Parse and extract information from PDF resumes:

# Analyze a resume from PDF
resume_data = analyze_resume("path/to/resume.pdf")

# Available extracted information
name = resume_data.name
email = resume_data.email
phone = resume_data.phone
skills = resume_data.skills
work_experience = resume_data.work_experience
education = resume_data.education
languages = resume_data.languages

The resume analysis extracts comprehensive candidate information including contact details, skills, work history, education background, and language proficiencies.

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "linkedin" '{"command":"uv","args":["--directory","<LOCAL_PATH>","run","linkedin.py"]}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

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

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

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

3. Restart Claude Desktop for the changes to take effect