Linkedin MCP

🚀 LinkedIn Profile Analyzer MCP

A powerful LinkedIn profile analyzer MCP (Machine Control 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.

🚀 Quick Start

To start using the LinkedIn Profile Analyzer MCP, you need to have Python 3.7+ installed, a RapidAPI key for the LinkedIn Data API, and access to Claude AI. Follow the steps below to get started.

1. Get RapidAPI Key

1. Visit LinkedIn Data API on RapidAPI.
2. Sign up or log in to RapidAPI.
3. Subscribe to the LinkedIn Data API.
4. Copy your RapidAPI key from the dashboard.

2. Installation

1. Clone the repository:

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

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:
   • Create a .env file.
   • Add your RapidAPI key:

RAPIDAPI_KEY=your_rapidapi_key_here

✨ Features

• Fetch and store LinkedIn posts for any public profile.
• Search through posts with keyword filtering.
• Get top performing posts based on engagement metrics.
• Filter posts by date range.
• Paginated access to stored posts.
• Easy integration with Claude AI.

📦 Installation

The installation steps are detailed in the "Quick Start" section above.

💻 Usage Examples

Basic Usage

# Example of using fetch_and_save_linkedin_posts
from your_module import fetch_and_save_linkedin_posts
username = "example_username"
result = fetch_and_save_linkedin_posts(username)
print(result)

Advanced Usage

# Example of getting top posts with custom metric and top_n
from your_module import get_top_posts
top_posts = get_top_posts(metric="Comment Count", top_n=3)
print(top_posts)

📚 Documentation

Project Structure

linkedin-mcp/
├── main.py              # Main MCP server implementation
├── mcp.json            # MCP configuration file
├── requirements.txt    # Python dependencies
├── .env               # Environment variables
└── README.md          # Documentation

MCP Configuration

The mcp.json file configures the LinkedIn MCP server:

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

Make sure to update the path in args to match your local file location.

Available Tools

1. fetch_and_save_linkedin_posts

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

fetch_and_save_linkedin_posts(username: str) -> str

2. get_saved_posts

Retrieves saved posts with pagination support.

get_saved_posts(start: int = 0, limit: int = 10) -> dict

3. search_posts

Searches posts for specific keywords.

search_posts(keyword: str) -> dict

4. get_top_posts

Returns top performing posts based on engagement metrics.

get_top_posts(metric: str = "Like Count", top_n: int = 5) -> dict

5. get_posts_by_date

Filters posts within a specified date range.

get_posts_by_date(start_date: str, end_date: str) -> dict

Using with Claude

1. Initialize the MCP server in your conversation with Claude.
2. Use the available tools through natural language commands.
3. Claude will help you interact with LinkedIn data using these tools.

API Integration

This project uses the following endpoint from the LinkedIn Data API:

• GET /get-profile-posts: Fetches posts from a LinkedIn profile
  • 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

🔧 Technical Details

The project is structured with a main MCP server implementation in main.py, a configuration file mcp.json, and dependencies managed in requirements.txt. The MCP server interacts with the LinkedIn Data API through the specified endpoint and uses environment variables for security.

📄 License

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

👨‍💻 Author

Rugved Patil

🔗 Repository

linkedin-mcp

🙏 Acknowledgments

• RapidAPI for providing LinkedIn data access.
• Anthropic for Claude AI capabilities.