team-headstart/headstarter-mcp-server

Headstarter LinkedIn Network MCP Server

A Model Context Protocol (MCP) Server that provides AI assistants with access to LinkedIn profile data from the Headstarter network. This server enables intelligent querying, searching, and analysis of LinkedIn profiles for recruiting, networking, and community building.

Add to Cursor

1. Go to Cursor Settings
2. Click on "Tools & Integrations"
3. Click on "Add MCP Server"
4. Paste the following JSON into the "MCP Servers" field:

{
  "mcpServers": {
    "Headstarter-MCP": {
      "url": "https://headstarter-mcp-server.vercel.app/sse"
    }
  }
}

Example Usage

What is MCP?

The Model Context Protocol (MCP) is a standardized way for AI applications to access external data and functionality. This server implements MCP to expose LinkedIn network data through tools and resources that AI assistants can use.

Overview

This MCP server provides access to a database of LinkedIn profiles from the Headstarter community, including:

• Personal Information: Names, usernames, profile pictures, headlines
• Work Status: Open to work status, hiring status, creator status
• Location Data: Cities and countries
• Professional Experience: Full-time and internship counts
• Education & Company Info: Most recent schools and companies
• Community Affiliation: Headstarter network connections

Available Tools

Core Tools

• linkedin-sql-query - Execute read-only SELECT queries against the LinkedIn network table
• get-linkedin-profile - Get a specific profile by username or URN
• search-linkedin-profiles - Advanced search with multiple filter options

Specialized Search Tools

• get-profiles-by-location - Find profiles by city or country
• get-open-to-work-profiles - Find people currently seeking opportunities
• get-hiring-profiles - Find people who are actively hiring
• get-creator-profiles - Find content creators and thought leaders
• get-headstarter-affiliated-profiles - Find Headstarter community members

Available Resources

• linkedin-network-schema - Database table schema and structure
• linkedin-network-stats - Network statistics and overview

More Usage Examples

Get Headstarter Alumni in New York

Use the get-headstarter-affiliated-profiles tool with city: "New York"

Search for Hiring Managers at Tech Companies

Use the get-hiring-profiles tool with company: "Google" or "Meta"

Custom SQL Queries

Use the linkedin-sql-query tool with:
query: "SELECT first_name, last_name, city, headline FROM hs_linkedin_network WHERE is_creator = true AND city ILIKE '%San Francisco%'"

Deployment on Vercel

This server is built with Next.js and uses the Vercel MCP Adapter.

Requirements

• Database: PostgreSQL database with hs_linkedin_network table
• Redis: Required for SSE transport (available as process.env.REDIS_URL)
• Fluid Compute: Enable for efficient long-running queries

Environment Variables

DATABASE_URL=postgresql://...
REDIS_URL=redis://...

Deployment Steps

1. Set up your PostgreSQL database with LinkedIn profile data
2. Enable Fluid compute in your Vercel project
3. Set maxDuration to 800 for Pro/Enterprise accounts in app/[transport]/route.ts
4. Configure environment variables
5. Deploy using the MCP Next.js template

Testing

Test your deployed server using the included client script:

node scripts/test-client.mjs https://your-deployment.vercel.app

Or use the MCP Inspector for interactive testing:

npx @modelcontextprotocol/inspector

Security Features

• Read-only Access: Only SELECT queries are allowed for data security
• Automatic LIMIT Protection: Queries are automatically limited to prevent large result sets
• Input Validation: All parameters are validated using Zod schemas
• Comprehensive Logging: Full request/response logging for monitoring

Use Cases

• Networking: Connect with Headstarter alumni in specific locations or companies
• Market Research: Analyze where Headstarter alumni are located and what companies they work for
• Content Collaboration: Find creators and thought leaders for collaborations

Development

Built with:

• Next.js 14 - Full-stack React framework
• TypeScript - Type-safe development
• Drizzle ORM - Database queries and schema management
• @vercel/mcp-adapter - MCP protocol implementation
• Zod - Runtime type validation

This MCP server enables AI assistants to intelligently search and analyze LinkedIn profile data, making it easier to find the right people for opportunities, collaborations, and community building.