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@felipfr/linkedin-mcpserver

MCP server for LinkedIn API access

LinkedIn MCP Server exposes LinkedIn API tools through Model Context Protocol so an agent can search profiles, find jobs, send messages, and read network stats. It is a TypeScript server with dependency injection, structured logging, and token-managed REST calls.

84 starsβ€’30 forksβ€’TypeScriptβ€’Updated 1y ago
Who it's for

Builders who want an MCP-compatible agent to use LinkedIn data and LinkedIn actions.

What it delivers

You can ask your agent to search LinkedIn, inspect profiles, and send messages without leaving your workflow.

What it does

Profile search

Find LinkedIn profiles with filters.

Profile retrieval

Fetch detailed information about a LinkedIn profile.

Job search

Search for job opportunities with custom criteria.

Messaging

Send messages to LinkedIn connections.

Network stats

Read connection counts and related analytics.

MCP integration

Connects via the Model Context Protocol for use with MCP-compatible assistants.

Token-managed REST client

Uses Axios with automatic token management for LinkedIn API calls.

How to get it

  1. 1Run
    # Install dependencies
    npm install
    
    # Run the development server
    npm run start:dev
    
    # Build the server
    npm run build
  2. 2Windows: %APPDATA%/Claude/claude_desktop_config.json
    {
      "mcpServers": {
        "linkedin-mcp-server": {
          "command": "/path/to/linkedin-mcp-server/build/index.js"
        }
      }
    }

README

🌐 LinkedIn MCP Server

A powerful Model Context Protocol server for LinkedIn API integration

πŸ“‹ Overview

LinkedIn MCP Server brings the power of the LinkedIn API to your AI assistants through the Model Context Protocol (MCP). This TypeScript server empowers AI agents to interact with LinkedIn data, search profiles, find jobs, and even send messages.

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs - think of it as a USB-C port for AI applications, connecting models to external data sources and tools.

✨ Features

πŸ” LinkedIn API Tools

  • Profile Search - Find LinkedIn profiles with advanced filters
  • Profile Retrieval - Get detailed information about LinkedIn profiles
  • Job Search - Discover job opportunities with customized criteria
  • Messaging - Send messages to LinkedIn connections
  • Network Stats - Access connection statistics and analytics

πŸ› οΈ Technical Highlights

  • TypeScript - Built with modern TypeScript for type safety and developer experience
  • Dependency Injection - Uses TSyringe for clean, testable architecture
  • Structured Logging - Comprehensive logging with Pino for better observability
  • MCP Integration - Implements the Model Context Protocol for seamless AI assistant connectivity
  • REST Client - Axios-powered API client with automatic token management

πŸš€ Development

Prerequisites

  • Node.js 20+
  • npm/yarn

Setup

# Install dependencies
npm install

# Run the development server
npm run start:dev

# Build the server
npm run build

πŸ“¦ Installation

To use with Claude Desktop or other MCP-compatible AI assistants:

Configuration

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

  • Windows: %APPDATA%/Claude/claude_desktop_config.json

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

πŸ”§ Debugging

MCP servers communicate over stdio which can make debugging challenging. Use the integrated MCP Inspector:

# Debug with MCP Inspector
npm run inspector

The Inspector provides a browser-based interface for monitoring requests and responses.

πŸ”’ Security

This server handles sensitive LinkedIn authentication credentials. Review the token management system to ensure it meets your security requirements.

πŸ“œ License

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

Files in the repo

Repository payloadβ€’10 top-level entries
  • src
  • .env.example
  • .gitignore
  • .prettierrc
  • eslint.config.js
  • LICENSE
  • package-lock.json
  • package.json
  • README.md
  • tsconfig.json

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