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MCP Server for LinkedIn Profile Scraping

Welcome to the MCP server designed to leverage the Fresh LinkedIn Profile Data API for extracting LinkedIn profile details. This server operates as a model context protocol (MCP) and provides a streamlined tool, get_profile, which takes a LinkedIn profile URL as input and returns the corresponding profile data in a structured JSON format.

Key Features

Profile Data Retrieval: Seamlessly fetch LinkedIn profile information, including skills, while keeping most additional details disabled for a cleaner output.
Asynchronous Operations: Utilizes httpx to perform non-blocking HTTP requests, ensuring efficient data fetching without hindering performance.
Environment Configuration: Automatically loads the RAPIDAPI_KEY from your environment variables using the dotenv package, simplifying setup and enhancing security.

Prerequisites

Python 3.7+ – Ensure you are using Python version 3.7 or higher.
MCP Framework: Make sure the MCP framework is installed.
Required Libraries: Install httpx, python-dotenv, and other dependencies.
RAPIDAPI_KEY: Obtain an API key from RapidAPI and add it to a .env file in your project directory (or set it in your environment).

Installation

Clone the Repository:

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

Install Dependencies:
```
uv add mcp[cli] httpx requests
```
Set Up Environment Variables:

Create a .env file in the project directory with the following content:
```
RAPIDAPI_KEY=your_rapidapi_key_here
```

Running the Server

To run the MCP server, execute:

uv run linkedin.py

The server will start and listen for incoming requests via standard I/O.

MCP Client Configuration

To connect your MCP client to this server, add the following configuration to your config.json. Adjust the paths as necessary for your environment:

{
  "mcpServers": {
     "linkedin-mcp": {
      "command": "/Users/shubham/.local/bin/uv",
      "args": [
          "--directory",
          "/Users/shubham/Documents/projects/linkedin-mcp",
          "run",
          "main.py"
      ]
  }
  }
}

Code Overview

Environment Setup: The server uses dotenv to load the RAPIDAPI_KEY required to authenticate with the Fresh LinkedIn Profile Data API.
API Call: The asynchronous function get_linkedin_data makes a GET request to the API with specified query parameters.
MCP Tool: The get_profile tool wraps the API call and returns formatted JSON data, or an error message if the call fails.
Server Execution: The MCP server is run with the stdio transport.

License

This project is licensed under the MIT License. See the LICENSE file for more details.