kempec110/linkedin-profile-mcp

LinkedIn Profile Explorer MCP

A comprehensive LinkedIn data extraction MCP (Model Context Protocol) server that integrates seamlessly with Claude Desktop. Fetch, explore, and search through LinkedIn posts and comments with powerful filtering and discovery tools.

🚀 What This Tool Does

This MCP server transforms Claude Desktop into a powerful LinkedIn data exploration platform. You can:

• 📊 Extract LinkedIn Posts: Fetch and browse posts from any public LinkedIn profile
• 💬 Comment Discovery: Extract and explore comments with engagement metrics
• 🔍 Smart Search: Search through posts and comments with keyword filtering
• 📈 Performance Insights: Discover top performing posts and comment engagement
• 📅 Date-Based Filtering: Filter content by specific date ranges
• 👥 Engagement Discovery: Identify most active commenters and popular discussions

🎯 Features

Post Extraction

• Fetch all posts from any public LinkedIn profile
• Search posts by keywords
• Discover top performing posts by likes and reactions
• Filter posts by date range
• Paginated access to large datasets

Comment Exploration

• Complete Comment Threads: Fetch posts with all their comments in one go
• Paginated Comment Loading: Handle large comment threads efficiently
• Comment Search: Find specific discussions across all saved data
• Engagement Discovery: Discover most active commenters and popular comments
• Comment Performance: Track likes and reactions on individual comments

📋 Prerequisites

• Python 3.7+ installed on your system
• Claude Desktop application
• RapidAPI account with LinkedIn Data API access

🛠️ Setup Guide

Step 1: Get Your RapidAPI Key

1. Visit RapidAPI: Go to LinkedIn Data API on RapidAPI
2. Create Account: Sign up for a free RapidAPI account if you don't have one
3. Subscribe to API: Click "Subscribe" on the LinkedIn Data API page
4. Choose Plan: Select a plan (free tier available for testing)
5. Get Your Key: Copy your RapidAPI key from the dashboard

Step 2: Download and Setup

1. Clone the Repository:

   git clone https://github.com/kempec110/linkedin-mcp.git
   cd linkedin-mcp
   

2. Install Python Dependencies:

   pip install -r requirements.txt
   

3. Create Environment File:

   • Create a file named .env in the project folder
   • Add your RapidAPI key:

   RAPIDAPI_KEY=your_rapidapi_key_here
   

Step 3: Configure Claude Desktop

1. Locate Claude Desktop Config:

   • Windows: %APPDATA%\Claude\claude_desktop_config.json
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Linux: ~/.config/Claude/claude_desktop_config.json

2. Edit Configuration File: Open the config file and add the LinkedIn MCP server:

   {
     "mcpServers": {
       "linkedin-analyzer": {
         "command": "python",
         "args": ["C:/path/to/your/linkedin-mcp/main.py"],
         "env": {
           "RAPIDAPI_KEY": "your_rapidapi_key_here"
         }
       }
     }
   }
   

   Important: Replace C:/path/to/your/linkedin-mcp/main.py with the actual path to your main.py file.

3. Restart Claude Desktop: Close and reopen Claude Desktop to load the new MCP server.

Step 4: Verify Installation

1. Open Claude Desktop
2. Start New Conversation
3. Test the Connection: Type something like:

   Can you help me explore LinkedIn posts? What tools do you have available?
   

If successful, Claude should show you the available LinkedIn data extraction tools.

📚 How to Use

Basic Post Extraction

1. Fetch Posts from a Profile:

   Fetch LinkedIn posts for the username "john-doe"
   

2. Search Through Posts:

   Search all saved posts for mentions of "artificial intelligence"
   

3. Get Top Performing Posts:

   Show me the top 5 posts by like count
   

Advanced Comment Exploration

1. Fetch Post with All Comments:

   Get all comments for LinkedIn post URN 7169084130104737792
   

2. Search Comments:

   Search all comments for the keyword "AI" and show engagement metrics
   

3. Explore Comment Engagement:

   Show me comment insights including top commenters and most liked comments
   

Data Filtering and Discovery

1. Filter by Date:

   Show me posts from January 2024 to March 2024
   

2. Get Comment Insights:

   Explore comment engagement patterns for post 7169084130104737792
   

🔧 Available Tools

Core Post Tools

Comment Exploration Tools (New!)

📁 Project Structure

linkedin-mcp/
├── main.py                              # Main MCP server implementation
├── requirements.txt                     # Python dependencies
├── .env                                # Environment variables (your API key)
├── README.md                           # This documentation
├── linkedin_posts.json                # Saved posts data
├── linkedin_posts_with_comments.json  # Posts with complete comment threads
└── linkedin_comments_paginated_*.json # Individual paginated comment files

🔑 Data Files Explained

• linkedin_posts.json: Basic post data without comments
• linkedin_posts_with_comments.json: Complete posts with all associated comments
• linkedin_comments_paginated_*.json: Individual files for each post's paginated comments

🚨 Troubleshooting

Common Issues

1. "MCP server not found":

   • Check that the path in claude_desktop_config.json is correct
   • Ensure main.py exists at the specified location
   • Restart Claude Desktop after config changes

2. "API key error":

   • Verify your RapidAPI key is correct in the .env file
   • Check that you're subscribed to the LinkedIn Data API on RapidAPI
   • Ensure the .env file is in the same directory as main.py

3. "No data found" errors:

   • Fetch posts first using fetch_and_save_linkedin_posts
   • Check that JSON files are being created in the project directory

Getting Help

If you encounter issues:

1. Check the file paths in your configuration
2. Verify your API key is working on RapidAPI
3. Ensure all dependencies are installed
4. Restart Claude Desktop after making changes

🔒 Privacy & Ethics

• This tool only accesses public LinkedIn data
• Always respect LinkedIn's terms of service
• Use responsibly and don't overwhelm the API with excessive requests
• Be mindful of privacy when exploring others' content

🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the Repository
2. Create a Feature Branch: git checkout -b feature/amazing-feature
3. Commit Changes: git commit -m 'Add amazing feature'
4. Push to Branch: git push origin feature/amazing-feature
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the file for details.

👨‍💻 Author

kempec110 - GitHub Profile

🙏 Acknowledgments

• rugvedp - Original LinkedIn MCP implementation and foundation
• RapidAPI - Providing reliable LinkedIn data access
• Anthropic - Claude AI platform and MCP framework
• LinkedIn Data API - Comprehensive LinkedIn data endpoints

📈 API Integration Details

This project integrates with the following RapidAPI endpoints:

Primary Endpoints

• GET /get-profile-posts: Fetch posts from LinkedIn profiles
• GET /get-profile-post-and-comments: Get complete post with all comments
• GET /get-profile-posts-comments: Paginated comment loading

API Configuration

• Base URL: https://linkedin-data-api.p.rapidapi.com
• Required Headers:
  • x-rapidapi-key: Your RapidAPI key
  • x-rapidapi-host: linkedin-data-api.p.rapidapi.com

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