📌Overview

Purpose: To provide a flexible and powerful framework for advanced interactions with LinkedIn's API, enhancing data integration for developers.

Overview: LinkedInMCP is a Model Context Protocol server that revolutionizes how developers retrieve and interact with LinkedIn data. It overcomes the limitations of traditional API methods by implementing an intelligent, context-aware system for managing professional network data.

Key Features:

Intelligent Context Management: Utilizes advanced context handling for more effective data interactions.
Secure, Typed Interactions: Ensures type safety in API calls to enhance reliability and security.
Extensible Architecture: Offers the ability to easily expand and customize functionalities based on specific user needs.
Standardized Protocol: Adheres to Model Context Protocol specifications, guaranteeing compatibility and interoperability.

LinkedInMCP: Revolutionizing LinkedIn API Interactions

🚀 Comprehensive Setup Guide

Prerequisites

Node.js 16+
LinkedIn Developer Account
MCP-Compatible LLM (Claude, OpenAI, etc.)

1. LinkedIn Developer Setup

Go to LinkedIn Developer Portal: https://www.linkedin.com/developers/
Create a new application
Obtain Client ID and Client Secret

Important: For detailed instructions on obtaining access tokens and authentication for LinkedIn API, refer to the official documentation: Getting Access to LinkedIn API

2. Project Installation

git clone https://github.com/Dishant27/linkedin-mcp-server.git
cd linkedin-mcp-server
npm install

3. Configuration Files

`.env` File

Create a .env file in the project root:

LINKEDIN_CLIENT_ID=your_client_id_here
LINKEDIN_CLIENT_SECRET=your_client_secret_here

`claude_desktop_config.json` (for Claude Desktop)

{
  "mcpServers": {
    "linkedin": {
      "command": "node",
      "args": ["/absolute/path/to/linkedin-mcp-server/dist/index.js"],
      "env": {
        "LINKEDIN_CLIENT_ID": "your_client_id",
        "LINKEDIN_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}

4. LLM Configuration Example

Claude.ai / Claude Desktop MCP Integration

{
  "tools": [
    {
      "name": "search-people",
      "description": "Search for LinkedIn profiles",
      "parameters": {
        "type": "object",
        "properties": {
          "keywords": {
            "type": "string",
            "description": "Keywords to search for in profiles"
          },
          "currentCompany": {
            "type": "array",
            "items": {"type": "string"},
            "description": "Filter by current company"
          },
          "industries": {
            "type": "array",
            "items": {"type": "string"},
            "description": "Filter by industries"
          },
          "location": {
            "type": "string", 
            "description": "Filter by location"
          }
        }
      }
    },
    {
      "name": "get-profile",
      "description": "Retrieve detailed LinkedIn profile information",
      "parameters": {
        "type": "object",
        "properties": {
          "publicId": {
            "type": "string",
            "description": "Public ID of the LinkedIn profile"
          },
          "urnId": {
            "type": "string", 
            "description": "URN ID of the LinkedIn profile"
          }
        }
      }
    }
  ]
}

5. Typical Workflow

Start the MCP Server:

npm run build
npm start

Example LLM Interactions:

> Find software engineers in San Francisco working at tech companies

# The LLM will use the search-people tool to:
# 1. Search LinkedIn profiles
# 2. Filter by location (San Francisco)
# 3. Filter by industry (Technology)
# 4. Return relevant profile details

📖 Learn More

Check out the detailed article explaining the concept and implementation:

LinkedIn MCP Server for LLMs

🚀 Project Background

LinkedInMCP is an innovative Model Context Protocol (MCP) server designed to transform how developers interact with LinkedIn's API. Born from the need for more flexible and powerful LinkedIn data integration, this project provides a robust, extensible framework for advanced LinkedIn data retrieval and interaction.

🌟 Project Vision

This solution creates a more intelligent, context-aware method of working with professional network data, overcoming limitations of traditional LinkedIn API approaches.

✨ Key Innovations

Intelligent Context Management using MCP
Secure, Typed LinkedIn API Interactions
Extensible Architecture for easy customization
Standardized Model Context Protocol compliance

🛠️ Technologies

TypeScript
Model Context Protocol
LinkedIn API
OAuth 2.0
Zod for Type Validation
Axios for HTTP Requests

🔍 Core Capabilities

Advanced People Search
Detailed Profile Retrieval
Job Market Intelligence
Messaging Capabilities
Secure Authentication Workflow

💼 Enterprise Use Cases

Talent Acquisition

Candidate Sourcing: Find qualified candidates based on skills and experience
Talent Pool Analysis: Map talent distribution across industries and regions
Outreach Automation: Streamline candidate contact
Competitive Intelligence: Analyze competitor recruitment patterns

Sales & Business Development

Lead Generation: Identify decision-makers at target companies
Account-Based Marketing: Build comprehensive profiles of key accounts
Relationship Mapping: Visualize organizational connection networks
Market Penetration Analysis: Track product/service adoption trends

Market Research

Industry Trends: Monitor employment shifts
Skill Demand Analysis: Track emerging technologies and skills
Organizational Insights: Map reporting structures and teams
Geographic Movement: Analyze talent migration between regions

Integration Capabilities

CRM Synchronization: Update LinkedIn data in CRM systems
ATS Enhancement: Enrich applicant tracking systems
Business Intelligence Integration: Feed LinkedIn data to analytics platforms
Custom Workflow Integration: Add LinkedIn features to internal tools via API

🔒 Security Framework

Authentication Security

OAuth 2.0 Token-Based Authentication
Automated Token Refresh Handling
Environment-Based Credential Isolation
Rate Limiting to Protect API Usage

Data Security

End-to-End Encryption of Data
Minimal Data Storage with On-the-Fly Processing
Selective Data Return to Limit Exposure
Automatic Data Sanitization to Prevent Injection Attacks

Compliance Features

GDPR Compliance Configurations
Enforced LinkedIn API Usage Policies
Audit Logging for Compliance Reporting
Data Governance Tools for Regulatory Management

📦 Generated Project Structure

linkedin-mcp-server/
│
├── src/
│   ├── index.ts         # Main server entry point
│   ├── auth.ts          # LinkedIn authentication handler
│   └── client.ts        # LinkedIn API interaction client
│
├── dist/                # Compiled JavaScript files
│   ├── index.js
│   ├── auth.js
│   └── client.js
│
├── .env                 # Secret environment variables
├── .env.example         # Template for environment variables
├── package.json         # Project dependencies and scripts
├── tsconfig.json        # TypeScript configuration
└── README.md            # Project documentation

🤝 Contributing

Contributions are welcome!

Fork the repository
Create your feature branch
Commit your changes
Push to the branch
Open a Pull Request

📄 License

Distributed under the MIT License.

Disclaimer: This project is an independent innovation and is not officially affiliated with LinkedIn or Microsoft.

linkedin-mcp-server