LinkedIn MCP server

This MCP server enables seamless integration between LinkedIn API and AI assistants through the Model Context Protocol. It allows AI agents to search LinkedIn profiles, find jobs, retrieve detailed profile information, send messages, and access connection statistics.

Installation

Prerequisites

• Node.js 20 or higher
• npm or yarn package manager

Setup and Configuration

Install the server dependencies:

npm install

Configure your AI assistant to use the LinkedIn MCP server:

For Claude Desktop

Edit the configuration file located at:

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

Add the following configuration:

{
  "mcpServers": {
    "linkedin-mcp-server": {
      "command": "/path/to/linkedin-mcp-server/build/index.js"
    }
  }
}

Make sure to replace /path/to/linkedin-mcp-server/build/index.js with the actual path to the built server file on your system.

Usage

Starting the Server

For development purposes, you can start the server with:

npm run start:dev

To build the server for production use:

npm run build

Available LinkedIn Tools

The server provides several LinkedIn API integrations:

• Profile Search: Find LinkedIn profiles using advanced filters
• Profile Retrieval: Access detailed information about specific LinkedIn profiles
• Job Search: Discover job opportunities with customized search criteria
• Messaging: Send messages to your LinkedIn connections
• Network Statistics: View connection statistics and analytics

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. Use the integrated MCP Inspector:

npm run inspector

This provides a browser-based interface for monitoring requests and responses between your AI assistant and the LinkedIn MCP server.

Security Considerations

This server handles sensitive LinkedIn authentication credentials. Be sure to properly secure your implementation, particularly regarding token management and storage.

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.