what is Linkedin-Scrap-MCP-Server?
Linkedin-Scrap-MCP-Server is a tool designed to fetch real-time LinkedIn profile information using the Model Context Protocol (MCP) server, integrating with the Fresh LinkedIn Profile Data API to return structured profile details.
how to use Linkedin-Scrap-MCP-Server?
To use the server, clone the repository, install the required dependencies, set up your environment variables with your RAPIDAPI_KEY, and run the server to start listening for incoming requests.
key features of Linkedin-Scrap-MCP-Server?
- Real-time retrieval of LinkedIn profile data including skills and public information.
- Asynchronous requests for efficient data fetching.
- Secure handling of API keys using environment variables.
use cases of Linkedin-Scrap-MCP-Server?
- Fetching up-to-date LinkedIn profiles for data analysis.
- Integrating LinkedIn data into applications for enhanced user profiles.
- Automating the collection of LinkedIn data for research purposes.
FAQ from Linkedin-Scrap-MCP-Server?
- How do I set up the server?
Clone the repository, install dependencies, set your RAPIDAPI_KEY, and run the server.
- What programming language is used?
The server is built using Python.
- What if I encounter API call errors?
The server will return a clear error message indicating the issue.
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!