LinkedIn Profile Analyzer MCP

A powerful LinkedIn profile analyzer MCP (Machine Control Protocol) server that interacts with LinkedIn's API to fetch, analyze, and manage LinkedIn posts data. This MCP is specifically designed to work with Claude AI.

Features

Fetch and store LinkedIn posts for any public profile
Search through posts with keyword filtering
Get top performing posts based on engagement metrics
Filter posts by date range
Paginated access to stored posts
Easy integration with Claude AI

Prerequisites

Python 3.7+
RapidAPI key for LinkedIn Data API
Claude AI access

Getting Started

1. Get RapidAPI Key

Visit LinkedIn Data API on RapidAPI
Sign up or log in to RapidAPI
Subscribe to the LinkedIn Data API
Copy your RapidAPI key from the dashboard

2. Installation

Clone the repository:

git clone https://github.com/rugvedp/linkedin-mcp.git
cd linkedin-mcp

Install dependencies:

pip install -r requirements.txt

Set up environment variables:
- Create a .env file
- Add your RapidAPI key:

RAPIDAPI_KEY=your_rapidapi_key_here

Project Structure

linkedin-mcp/
├── main.py              # Main MCP server implementation
├── mcp.json            # MCP configuration file
├── requirements.txt    # Python dependencies
├── .env               # Environment variables
└── README.md          # Documentation

MCP Configuration

The mcp.json file configures the LinkedIn MCP server:

{
  "mcpServers": {
    "LinkedIn Updated": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "path/to/your/script.py"
      ]
    }
  }
}

Make sure to update the path in args to match your local file location.

Available Tools

1. fetch_and_save_linkedin_posts

Fetches LinkedIn posts for a given username and saves them locally.

fetch_and_save_linkedin_posts(username: str) -> str

2. get_saved_posts

Retrieves saved posts with pagination support.

get_saved_posts(start: int = 0, limit: int = 10) -> dict

3. search_posts

Searches posts for specific keywords.

search_posts(keyword: str) -> dict

4. get_top_posts

Returns top performing posts based on engagement metrics.

get_top_posts(metric: str = "Like Count", top_n: int = 5) -> dict

5. get_posts_by_date

Filters posts within a specified date range.

get_posts_by_date(start_date: str, end_date: str) -> dict

Using with Claude

Initialize the MCP server in your conversation with Claude
Use the available tools through natural language commands
Claude will help you interact with LinkedIn data using these tools

API Integration

This project uses the following endpoint from the LinkedIn Data API:

GET /get-profile-posts: Fetches posts from a LinkedIn profile
- Base URL: https://linkedin-data-api.p.rapidapi.com
- Required Headers:
  - x-rapidapi-key: Your RapidAPI key
  - x-rapidapi-host: linkedin-data-api.p.rapidapi.com

Contributing

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Rugved Patil

Repository

linkedin-mcp

Acknowledgments

RapidAPI for providing LinkedIn data access
Anthropic for Claude AI capabilities