# LinkedIn Intelligence & Research MCP Server

> A production-ready Model Context Protocol (MCP) server for LinkedIn Profile & Activity Intelligence, Deep 15-Dimension Post Analysis, Business Signal Detection (GA4, GTM, Meta CAPI), Prospect ICP Matching, and Sales Opportunity Discovery.

![Build Status](https://img.shields.io/badge/Build-Passing-brightgreen.svg)
![Vitest](https://img.shields.io/badge/Tests-8%20Passed-green.svg)
![TypeScript](https://img.shields.io/badge/TypeScript-5.7-blue)
![MCP SDK](https://img.shields.io/badge/MCP-v1.5.0-blue)
![License](https://img.shields.io/badge/License-MIT-green)

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## 📌 Features

- 🕵️ **`linkedin_research_profile` Orchestrator**: High-level command that runs end-to-end profile research, normalizes activity, extracts topics, detects signals, evaluates ICP match, and returns actionable outreach angles.
- 👤 **Static Profile Intelligence (`linkedin_analyze_profile`)**: Analyzes headline, about section, role, company size, and positioning.
- 📝 **Deep Post Analyzer (`linkedin_analyze_post`)**: 15-dimension post matrix (Topic, Post Type, Hook, Audience, Intent, Tone, CTA, Pain Points, Tools, Sentiment, Commercial Intent).
- 📡 **Business Signal Detector (`linkedin_detect_business_signals`)**: Identifies Growth (hiring, expansion), Marketing (funnels, CRM), and Technical Tracking signals (GA4, GTM, Meta CAPI, Attribution).
- 🎯 **ICP Prospect Matcher (`linkedin_analyze_prospect`)**: Evaluates profile fit against custom Ideal Customer Profile rules with confidence scoring.
- 💡 **Opportunity Finder (`linkedin_find_opportunities`)**: Maps user discussions into technical tracking service opportunities (e.g. GA4 offline conversion tracking).
- 📅 **Activity Timeline (`linkedin_build_activity_timeline`)**: Constructs a chronological summary of professional activity over custom date ranges.
- 📑 **MCP Resources (`linkedin://`)**: Exposes static/dynamic URIs (`linkedin://profiles/{id}`, `linkedin://profiles/{id}/analysis`, `linkedin://profiles/{id}/signals`).
- 💬 **MCP Prompts**: Includes reusable prompt workflows (`/profile-analysis`, `/prospect-analysis`, `/find-tracking-opportunities`).

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## 🛠️ Installation & Build

```bash
# Install dependencies
npm install

# Run Vitest unit test suite
npm test

# Compile TypeScript to dist/
npm run build

# Start MCP server on stdio transport
npm start
```

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## ⚙️ MCP Client Configuration

### Cursor IDE Configuration
Add to `.cursor/mcp.json` or Cursor MCP Settings:

```json
{
  "mcpServers": {
    "linkedin-intelligence-mcp": {
      "command": "node",
      "args": ["C:/Users/FLS/.gemini/antigravity/scratch/linkedin-mcp-server/dist/index.js"]
    }
  }
}
```

### Antigravity Configuration
Add to `.gemini/antigravity/mcp_config.json`:

```json
{
  "mcpServers": {
    "linkedin-intelligence-mcp": {
      "command": "node",
      "args": ["C:/Users/FLS/.gemini/antigravity/scratch/linkedin-mcp-server/dist/index.js"]
    }
  }
}
```

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## 📄 License
Licensed under the [MIT License](LICENSE).