LinkedIn Profile Analyzer

A powerful LinkedIn profile analyzer MCP (Model Context Protocol) server that interacts with LinkedIn's API to fetch, analyze, and manage LinkedIn posts data. This MCP is specifically designed to work with Claude AI.

python

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "rugvedp-linkedin-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "path/to/your/script.py"
      ],
      "env": {
        "RAPIDAPI_KEY": "YOUR_API_KEY"
      }
    }
  }
}

You can run a LinkedIn Profile Analyzer MCP server that fetches, stores, searches, and analyzes public LinkedIn posts. It integrates with Claude AI to empower conversations with up-to-date post data and engagement insights, making it easier to explore profiles, compare posts, and surface top-performing content.

How to use

Start by launching the MCP server through your chosen client and connect it to Claude AI. Use the provided tools to fetch posts for a LinkedIn username, view saved posts with pagination, search posts by keyword, filter by date, and retrieve top-performing posts based on engagement. You can combine these capabilities to build workflows such as fetching recent posts for a profile, filtering by a date range, and then highlighting the most engaging content.

How to install

Prerequisites you need to prepare before running the MCP server.

Install Python 3.7 or newer and ensure it is available in your system path.

Obtain a RapidAPI key for the LinkedIn Data API and ensure Claude AI access if you plan to integrate with Claude.

Configuration and start

Configure the MCP server using the example configuration snippet below. This defines how the MCP runtime should start the LinkedIn MCP server.

{
  "mcpServers": {
    "LinkedIn Updated": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "path/to/your/script.py"
      ]
    }
  }
}

Environment variables

Create a local environment file to store sensitive keys. The example uses RAPIDAPI_KEY to access the LinkedIn Data API.

RAPIDAPI_KEY=your_rapidapi_key_here

Available tools

fetch_and_save_linkedin_posts

Fetches LinkedIn posts for a given username and saves them locally.

get_saved_posts

Retrieves saved posts with pagination support.

search_posts

Searches posts for specific keywords.

get_top_posts

Returns top performing posts based on engagement metrics.

get_posts_by_date

Filters posts within a specified date range.

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