LinkedIn Lead Automation MCP Server

LinkedIn Lead Automation MCP Server

Production-grade LinkedIn Lead Automation MCP (Model Context Protocol) Server with real-time search, analysis, scoring, messaging, and automated follow-up sequences.

Features

• 🔍 Lead Discovery: Search LinkedIn profiles by keywords, location, and filters
• 📊 Profile Analysis: Extract and analyze complete LinkedIn profile data
• 🎯 AI-Powered Scoring: Intelligent lead scoring (0-100) based on profile data
• 💬 Message Generation: Hyper-personalized message generation using AI
• 📨 Automated Messaging: Send connection requests and direct messages
• 🔄 Follow-up Sequences: Automated multi-stage follow-up campaigns
• 🔐 API Key Management: Secure tier-based access control
• 📈 Usage Tracking: Monitor API usage and enforce tier limits
• 🗄️ PostgreSQL Support: Built with Neon PostgreSQL for production use

Architecture

• MCP Server (src/index.js): Stdio-based MCP protocol server
• HTTP API (src/http-server.js): RESTful HTTP API wrapper
• Background Worker (src/worker.js): Automated follow-up sequence processor
• Database (src/database-pg.js): PostgreSQL database layer
• LinkedIn Automation (src/linkedin.js): Chrome DevTools Protocol integration
• AI Service (src/ai.js): Anthropic Claude on Vertex AI (Google Cloud) integration for scoring and messaging

Prerequisites

• Node.js 18+
• PostgreSQL (Neon or any PostgreSQL 14+)
• Chrome/Chromium browser with remote debugging enabled
• Google Cloud SDK with gcloud CLI (for Vertex AI authentication)
• GCP Project with Vertex AI API enabled

Installation

# Clone the repository
git clone https://github.com/vikram-agentic/linkedin-mcp.git
cd linkedin-mcp

# Install dependencies
npm install

# Create .env file
cp .env.example .env

Configuration

Create a .env file with the following variables:

# Database (Neon PostgreSQL)
DATABASE_URL=postgresql://user:password@host/database?sslmode=require

# Google Cloud / Vertex AI Configuration
GCP_PROJECT_ID=amgn-app
GCP_LOCATION=global
ANTHROPIC_MODEL_ID=claude-sonnet-4-5

# Server Configuration
PORT=3001

# Chrome DevTools Protocol (optional, for browser automation)
CDP_URL=http://localhost:9222

Database Setup

1. Create a Neon PostgreSQL database (or use any PostgreSQL 14+)
2. Run the schema in Neon SQL Editor:

# Use schema-neon.sql for Neon PostgreSQL
cat database/schema-neon.sql

Copy and paste the SQL from database/schema-neon.sql into Neon SQL Editor and execute it.

Usage

Start MCP Server (Stdio)

npm start

This starts the MCP server using stdio transport. Connect via MCP clients like Claude Desktop.

Start HTTP API Server

npm run http

This starts the HTTP API server on port 3001 (or PORT from .env).

Start Background Worker

npm run worker

This starts the automated follow-up sequence processor.

API Endpoints

Health Check

GET /health

Generate API Key

POST /api/generate-key
Body: { "tier": "starter" | "professional" | "agency" | "enterprise" }

Connect Browser

POST /api/browser/connect
Body: { "cdp_url": "http://localhost:9222" }

Setup LinkedIn Session

POST /api/session/setup
Body: { "api_key": "...", "li_at_cookie": "..." }

Search Leads

POST /api/leads/search
Body: { "api_key": "...", "keywords": "...", "location": "...", "limit": 25 }

Analyze Profile

POST /api/leads/analyze
Body: { "api_key": "...", "profile_url": "..." }

Score Lead

POST /api/leads/score
Body: { "api_key": "...", "profile_url": "..." }

Generate Message

POST /api/messages/generate
Body: {
  "api_key": "...",
  "profile_url": "...",
  "value_proposition": "...",
  "message_type": "connection" | "direct"
}

Send Message

POST /api/messages/send
Body: {
  "api_key": "...",
  "profile_url": "...",
  "message": "...",
  "is_connection_request": false
}

Create Follow-up Sequence

POST /api/sequences/create
Body: {
  "api_key": "...",
  "profile_url": "...",
  "initial_message": "...",
  "num_followups": 3
}

Get Leads

GET /api/leads?api_key=...

Get Usage Stats

GET /api/usage?api_key=...

MCP Tools

When using as an MCP server, the following tools are available:

• connect_browser: Connect to Chrome via CDP
• setup_session: Authenticate LinkedIn session
• search_leads: Search for LinkedIn leads
• analyze_profile: Extract profile data
• score_lead: AI-powered lead scoring
• generate_message: Generate personalized messages
• send_message: Send messages to profiles
• create_followup_sequence: Create automated sequences
• generate_api_key: Generate API keys

Tier Limits

Development

# Generate a test API key
npm run generate-key

# Run in development mode
npm start

Production Deployment

Deploy to Vercel

1. Connect Repository to Vercel:

   # Install Vercel CLI
   npm i -g vercel
   
   # Login and deploy
   vercel login
   vercel --prod
   

2. Set Environment Variables in Vercel Dashboard:

   • DATABASE_URL: Your Neon PostgreSQL connection string
   • GCP_PROJECT_ID: Your Google Cloud project ID
   • GCP_LOCATION: Location (default: global)
   • ANTHROPIC_MODEL_ID: Model ID (default: claude-sonnet-4-5)

3. For GCP Authentication:Since Vercel doesn't support gcloud auth, you have two options:

   Option A: Use Service Account (Recommended)

   • Create a GCP Service Account with Vertex AI permissions
   • Download the JSON key file
   • Convert to base64 and set as GOOGLE_APPLICATION_CREDENTIALS env var
   • Update src/ai.js to use service account auth

   Option B: Use API Key (Alternative)

   • Generate a Vertex AI API key
   • Set as VERTEX_AI_API_KEY environment variable

Deploy with PM2 (Self-Hosted)

1. Set up PostgreSQL database (recommended: Neon)
2. Configure environment variables
3. Run database schema
4. Deploy using PM2:

pm2 start src/http-server.js --name linkedin-mcp-api
pm2 start src/worker.js --name linkedin-mcp-worker

Security Notes

• ⚠️ Never commit .env files - they contain sensitive credentials
• 🔐 API keys are hashed using bcrypt
• 🔒 All database queries use parameterized statements
• 🛡️ CORS is configured for production use

License

MIT License - see LICENSE file for details

Author

Agentic AI AMRO Ltd

Support

For issues and feature requests, please open an issue on GitHub.