Linkedin-Scrap-MCP-Server

Name: Linkedin-Scrap-MCP-Server
Author: itsShashankSrivastava

@itsShashankSrivastava

Repository

About Linkedin-Scrap-MCP-Server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Linkedin-Scrap-MCP-Server": {
      "command": "uv",
      "args": [
        "run",
        "linkedin.py"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Linkedin-Scrap-MCP-Server?

Linkedin-Scrap-MCP-Server is an MCP (Model Context Protocol) server that fetches real-time LinkedIn profile information via the Fresh LinkedIn Profile Data API. It exposes a single tool, get_profile, which accepts a LinkedIn profile URL and returns structured JSON data including skills and other basic public settings. It is intended for developers who need programmatic access to LinkedIn profile data within MCP-compatible applications.

How to use Linkedin-Scrap-MCP-Server?

Clone the repository, install dependencies with uv add mcp[cli] httpx requests, and set the RAPIDAPI_KEY environment variable. Start the server with uv run linkedin.py. Configure your MCP client by adding an entry to config.json with the path to uv and the server directory.

Key features of Linkedin-Scrap-MCP-Server

Real-time LinkedIn profile data including skills
Asynchronous HTTP requests via httpx
Secure API key management with dotenv
Single get_profile tool for easy integration
Error messages returned on API failures

Use cases of Linkedin-Scrap-MCP-Server

Fetch real-time LinkedIn profile information for analysis
Retrieve skills and public profile data programmatically
Integrate LinkedIn data into MCP-based assistants or agents
Automate profile data collection for recruitment or research

FAQ from Linkedin-Scrap-MCP-Server

What is the RAPIDAPI_KEY and how do I get it?

The RAPIDAPI_KEY is required to authenticate requests to the Fresh LinkedIn Profile Data API. You must sign up at RapidAPI, subscribe to that API, and then set the key as an environment variable.

What are the runtime dependencies?

Python 3.7+, the MCP framework, httpx, and python-dotenv are required. The server also uses uv for dependency management and execution.

What happens if the API call fails?

If the LinkedIn API request fails, the get_profile tool returns a clear error message explaining what went wrong. A missing RAPIDAPI_KEY will raise a ValueError.

What data does the `get_profile` tool return?

It returns structured JSON containing profile details like skills and other basic public settings. Additional extended fields are disabled by default.

Comments

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