LinkedIn Profile Scraper MCP Server

This MCP server uses the Fresh LinkedIn Profile Data API to fetch LinkedIn profile information. It is implemented as a model context protocol (MCP) server and exposes a single tool, get_profile, which accepts a LinkedIn profile URL and returns the profile data in JSON format.

Features

Fetch Profile Data: Retrieves LinkedIn profile information including skills and other settings (with most additional details disabled).
Asynchronous HTTP Requests: Uses httpx for non-blocking API calls.
Environment-based Configuration: Reads the RAPIDAPI_KEY from your environment variables using dotenv.

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/AIAnytime/Awesome-MCP-Server
cd linkedin_profile_scraper

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_profile_scraper": {
      "command": "C:/Users/aiany/.local/bin/uv",
      "args": [
        "--directory",
        "C:/Users/aiany/OneDrive/Desktop/YT Video/linkedin-mcp/project",
        "run",
        "linkedin.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.

Troubleshooting

Missing RAPIDAPI_KEY: If the key is not set, the server will raise a ValueError. Make sure the key is added to your .env file or set in your environment.
API Errors: If the API request fails, the tool will return a message indicating that the profile data could not be fetched.

License

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

Features

Fetch Profile Data: Retrieves LinkedIn profile information including skills and other settings (with most additional details disabled).

Asynchronous HTTP Requests: Uses httpx for non-blocking API calls.

Environment-based Configuration: Reads the RAPIDAPI_KEY from your environment variables using dotenv.

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/AIAnytime/Awesome-MCP-Server
cd linkedin_profile_scraper

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

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_profile_scraper": { "command": "C:/Users/aiany/.local/bin/uv", "args": [ "--directory", "C:/Users/aiany/OneDrive/Desktop/YT Video/linkedin-mcp/project", "run", "linkedin.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.