What is Linkedin Scrap Mcp Server

Linkedin-Scrap-MCP-Server is a server that allows users to easily fetch real-time LinkedIn profile information using the Model Context Protocol (MCP). It integrates with the Fresh LinkedIn Profile Data API to provide structured JSON data about LinkedIn profiles.

Use cases

Use cases include extracting skills and profile information for recruitment purposes, analyzing LinkedIn profiles for market research, and integrating LinkedIn data into applications for enhanced user insights.

How to use

To use the Linkedin-Scrap-MCP-Server, clone the repository, install the required dependencies, set up your environment variables with your RAPIDAPI_KEY, and run the server using the command ‘uv run linkedin.py’.

Key features

Key features include real-time LinkedIn data retrieval, asynchronous requests for efficient processing, and secure API key handling using environment variables.

Where to use

The Linkedin-Scrap-MCP-Server can be used in various fields such as data analysis, recruitment, marketing, and any application that requires real-time LinkedIn profile data.

Linkedin-Scrap-MCP-Server

Easily fetch real-time LinkedIn profile information using our MCP (Model Context Protocol) server. This server integrates with the Fresh LinkedIn Profile Data API to return profile details like skills and other basic settings. It exposes a single tool—get_profile—that accepts a LinkedIn profile URL and responds with structured JSON data.

🚀Features

• Real-Time LinkedIn Data: Retrieve up-to-date profile info including skills and core public data (additional extended fields are disabled by default).
• Asynchronous Requests: Built with httpx for efficient, non-blocking HTTP calls.
• Secure API Key Handling: Uses environment variables via dotenv for safe configuration of your RAPIDAPI_KEY.

⚙️Requirements

Before you get started, make sure you have:

• Python 3.7+
• MCP Framework installed
• Required libraries: httpx, python-dotenv
• RAPIDAPI_KEY: Sign up at RapidAPI and subscribe to the Fresh LinkedIn Profile Data API, then grab your API key.

📦Installation

1. Clone the repository:

git clone https://github.com/itsShashankSrivastava/Linkedin-Scrap-MCP-Server

2. Install dependencies:

uv add mcp[cli] httpx requests

3. Set up your environment variables:

RAPIDAPI_KEY=your_rapidapi_key_here

▶️ Running the Server

To start the MCP server:

uv run linkedin.py

This will launch the server and begin listening for incoming requests over standard I/O.

🤖 MCP Client Configuration

To connect your MCP client to the server, update your config.json with the following:

{
  "mcpServers": {
    "linkedin_profile_scraper": {
      "command": "C:/Users/shashank.srivastava/.local/bin/uv", 
      "args": [
        "--directory",
        "C:/Users/shashank.srivastava/Desktop/linkedin-scrap",  
        "run",
        "linkedin.py" 
      ]
    }
  }
}

💡 Adjust the paths as necessary based on where your server is located.

🧠 How It Works

• Environment Setup: Uses dotenv to load your RAPIDAPI_KEY.
• API Integration: Makes asynchronous GET requests to the Fresh LinkedIn Profile Data API using httpx.
• MCP Tool - get_profile: Wraps the API logic and returns either a clean JSON object or an error message if the request fails.
• Execution: Runs using the standard I/O (stdio) transport method of the MCP server framework.

🛠 Troubleshooting

• Missing RAPIDAPI_KEY: If not set, the server will raise a ValueError. Double-check your .env file or system environment variables.
• API Call Errors: If the LinkedIn API request fails, the tool will return a clear error message indicating what went wrong.

Feel free to ⭐️ the repo if you find it helpful or open an issue if you need support!