by Ayesha0300
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.
httpx
for non-blocking API calls.RAPIDAPI_KEY
from your environment variables using dotenv
.httpx
, python-dotenv
, and other dependencies..env
file in your project directory (or set it in your environment).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
To run the MCP server, execute:
uv run linkedin.py
The server will start and listen for incoming requests via standard I/O.
To connect your MCP client to this server, add the following configuration to your config.json
. Adjust the paths as necessary for your environment:
*Configuration content*
dotenv
to load the RAPIDAPI_KEY
required to authenticate with the Fresh LinkedIn Profile Data API.get_linkedin_data
makes a GET request to the API with specified query parameters.get_profile
tool wraps the API call and returns formatted JSON data, or an error message if the call fails.stdio
transport.ValueError
. Make sure the key is added to your .env
file or set in your environment.This project is licensed under the MIT License. See the LICENSE file for more details.
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