headhunter-mcp-server

stricher05/headhunter-mcp-server

3.1

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HeadHunter MCP Server is a comprehensive system designed for executive technology leadership placement, offering AI-powered tools for job hunting and career advancement.

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HeadHunter MCP Server

Executive Technology Leadership Placement System - A comprehensive MCP server for researching companies, analyzing job opportunities, and preparing for executive technology roles.

License: MIT GitHub

Overview

HeadHunter MCP Server provides AI-powered tools for executive job hunting and career advancement in technology leadership roles. It offers comprehensive company research, LinkedIn intelligence, revenue engine analysis, and strategic interview preparation.

Key Features

  • 🏢 Company Research: Deep analysis of business models, technology stacks, and strategic challenges
  • 🔗 LinkedIn Intelligence: ToS-compliant relationship mapping and warm introduction pathfinding
  • 💰 Revenue Engine Mapping: P&L impact analysis for technical decisions and platform investments
  • 🎯 Interview Preparation: Architecture scenarios, business cases, and executive-level questions
  • 📋 Executive Briefing: Comprehensive summaries and strategic preparation materials
  • 📅 30-60-90 Day Planning: Strategic onboarding plans for executive roles

Installation

NPM Package

npm install -g @gnivildev/headhunter-mcp-server

Claude Desktop Configuration

Add to your Claude Desktop MCP configuration (%APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "headhunter": {
      "command": "npx",
      "args": ["-y", "@gnivildev/headhunter-mcp-server"],
      "env": {
        "NODE_ENV": "production"
      }
    }
  }
}

Development Setup

git clone https://github.com/stricher05/headhunter-mcp-server
cd headhunter-mcp-server
npm install
npm run build
npm run dev

Testing the Installation

# Test global CLI installation
headhunter-mcp --help

# Test MCP server functionality
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | headhunter-mcp

Available Tools

research_company

Research a company for executive technology roles with comprehensive business and technical analysis.

Parameters:

  • company (required): Company name to research
  • role (optional): Target role (default: "VP Engineering")
  • focus_areas (optional): Specific areas to analyze

Example:

{
  "name": "research_company",
  "arguments": {
    "company": "Stripe",
    "role": "CTO",
    "focus_areas": ["revenue_engine", "tech_stack", "team_analysis"]
  }
}

analyze_revenue_engine

Map how company's technical systems drive revenue and costs for P&L impact analysis.

Parameters:

  • company (required): Company name to analyze
  • business_model (optional): Primary business model
  • focus (optional): Analysis focus area

Example:

{
  "name": "analyze_revenue_engine",
  "arguments": {
    "company": "Notion",
    "business_model": "B2B SaaS",
    "focus": "growth_levers"
  }
}

linkedin_intelligence

Analyze LinkedIn relationships and find warm introduction paths (ToS-compliant).

Parameters:

  • company (required): Target company name
  • role (optional): Target role for team research
  • export_data (optional): Path to LinkedIn export data
  • research_depth (optional): Depth of research

Example:

{
  "name": "linkedin_intelligence",
  "arguments": {
    "company": "Figma",
    "role": "VP Engineering",
    "research_depth": "comprehensive"
  }
}

interview_preparation

Generate comprehensive interview preparation including scenarios and questions.

Parameters:

  • company (required): Company name
  • role (required): Target role
  • interview_type (optional): Type of preparation
  • focus_areas (optional): Specific preparation areas

Example:

{
  "name": "interview_preparation",
  "arguments": {
    "company": "Airbnb",
    "role": "Head of Platform",
    "interview_type": "comprehensive",
    "focus_areas": ["architecture", "leadership", "strategy"]
  }
}

executive_brief

Generate executive summary brief for target role application.

Parameters:

  • company (required): Company name
  • role (required): Target role
  • application_stage (optional): Current stage
  • include_sections (optional): Sections to include

Example:

{
  "name": "executive_brief",
  "arguments": {
    "company": "Datadog",
    "role": "VP Engineering",
    "application_stage": "interview"
  }
}

create_30_60_90_plan

Create detailed 30-60-90 day plan for executive technology role.

Parameters:

  • company (required): Company name
  • role (required): Target role
  • team_size (optional): Expected team size
  • key_challenges (optional): Known challenges
  • focus_style (optional): Leadership style focus

Example:

{
  "name": "create_30_60_90_plan",
  "arguments": {
    "company": "Slack",
    "role": "CTO",
    "team_size": 120,
    "focus_style": "transformation"
  }
}

Real-World Example

McCain Foods Head of AI Analysis

We tested the HeadHunter system with a real LinkedIn job posting for McCain Foods' Head of AI position. The analysis generated:

  • Company Intelligence: $10B+ revenue, global leader in frozen potato products, 60+ countries
  • Revenue Engine Mapping: $50-100M AI opportunity in supply chain optimization
  • Strategic Positioning: Ground-floor AI leadership role with significant business impact potential
  • Interview Strategy: Focus on traditional industry transformation and P&L impact
  • 30-60-90 Plan: Supply chain quick wins → manufacturing AI → comprehensive transformation

See the complete analysis in our .

Usage Examples

Basic Company Research

// Research a company for VP Engineering role
const result = await client.callTool({
  name: "research_company",
  arguments: {
    company: "Stripe",
    role: "VP Engineering",
    focus_areas: ["technology", "business_model", "challenges"]
  }
});

Complete Application Preparation

// 1. Company research
const research = await client.callTool({
  name: "research_company",
  arguments: { company: "Notion", role: "CTO" }
});

// 2. Revenue engine analysis
const revenue = await client.callTool({
  name: "analyze_revenue_engine",
  arguments: { company: "Notion", focus: "comprehensive" }
});

// 3. LinkedIn intelligence
const linkedin = await client.callTool({
  name: "linkedin_intelligence",
  arguments: { company: "Notion", research_depth: "detailed" }
});

// 4. Interview preparation
const interview = await client.callTool({
  name: "interview_preparation",
  arguments: { company: "Notion", role: "CTO" }
});

// 5. Executive brief
const brief = await client.callTool({
  name: "executive_brief",
  arguments: { company: "Notion", role: "CTO" }
});

Privacy & Compliance

LinkedIn Terms of Service Compliance

  • Export-First: Prioritizes LinkedIn data export over automated scraping
  • Rate Limiting: Respects platform rate limits and usage guidelines
  • User Consent: Only processes data with explicit user permission
  • Public Data Only: When not using exports, only accesses publicly available information

Data Privacy

  • Local Processing: All data processing happens locally by default
  • No Data Storage: Does not store personal or professional data persistently
  • Encryption: Sensitive data encrypted in transit and at rest
  • GDPR Compliant: Respects user privacy rights and data protection regulations

Development

Project Structure

mcp-server/
├── src/
│   ├── index.ts          # Main MCP server implementation
│   ├── tools/            # Individual tool implementations
│   │   ├── company-research.ts
│   │   ├── linkedin-intelligence.ts
│   │   ├── revenue-engine.ts
│   │   ├── interview-prep.ts
│   │   └── executive-brief.ts
│   └── utils/            # Utility modules
│       ├── template-engine.ts
│       └── data-sources.ts
├── templates/            # Document templates
├── package.json
├── tsconfig.json
└── README.md

Building and Testing

# Install dependencies
npm install

# Build TypeScript
npm run build

# Run in development mode (for debugging)
npm run dev

# Test MCP server functionality
npm run test

# Lint code
npm run lint

# Clean build artifacts
npm run clean

Publishing Process

# Build the package
npm run build

# Test locally
npm link
npm link @gnivildev/headhunter-mcp-server

# Publish to npm (requires 2FA)
npm login
npm publish --access public --otp=XXXXXX

# Push to GitHub
git add .
git commit -m "feat: update package version"
git push origin main

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Development Tools

  • TypeScript: Strongly typed development
  • ESLint: Code quality and consistency
  • Jest: Testing framework
  • GitHub Actions: CI/CD pipeline
  • npm: Package management and publishing

Configuration

Environment Variables

# Optional: API keys for enhanced data sources
CLEARBIT_API_KEY=your_clearbit_key
CRUNCHBASE_API_KEY=your_crunchbase_key
LINKEDIN_CLIENT_ID=your_linkedin_client_id
LINKEDIN_CLIENT_SECRET=your_linkedin_client_secret

# Optional: Custom data source URLs
COMPANY_DATA_API_URL=https://api.example.com
NEWS_API_KEY=your_news_api_key

Advanced Configuration

Create a .headhunterrc.json file in your project directory:

{
  "dataSources": {
    "enableWebScraping": true,
    "respectRobotsTxt": true,
    "rateLimitMs": 1000
  },
  "templates": {
    "customTemplatesPath": "./custom-templates"
  },
  "privacy": {
    "enableDataCaching": false,
    "encryptSensitiveData": true
  }
}

Use Cases

Executive Job Search

  • Research target companies for strategic fit
  • Map revenue impact of technical decisions
  • Find warm introduction paths to hiring managers
  • Prepare for technical and leadership interviews
  • Create compelling 30-60-90 day plans

Career Development

  • Analyze industry trends and company positioning
  • Understand revenue models and business challenges
  • Build professional network strategically
  • Prepare for internal promotions and role transitions

Strategic Planning

  • Competitive analysis for technology decisions
  • Business case development for platform investments
  • Team scaling and organizational design
  • Cross-functional collaboration strategies

Support & Resources

Documentation

Community

Professional Services

  • Custom implementation and integration
  • Executive coaching and career consulting
  • Enterprise licensing and white-label solutions
  • Training and workshops

License

This project is licensed under the MIT License - see the file for details.

Acknowledgments

  • Built with Model Context Protocol (MCP)
  • Inspired by executive technology leadership best practices
  • Community contributions and feedback
  • Privacy-first approach to professional data handling

HeadHunter MCP Server - Empowering executive technology leadership through AI-powered career intelligence.

Live Package: @gnivildev/headhunter-mcp-server Source Code: GitHub Repository Author: stricher05 (gnivildev)