MCP Website Chatbot

A production-grade AI chatbot for srinivasanramanujam.sbs with live data retrieval via MCP (Model Context Protocol) and RAG (Retrieval-Augmented Generation).

🚀 Features

• Live Data Integration – MCP tools for real-time information retrieval
• RAG Support – Static knowledge base from website content, blogs, and FAQs
• Hallucination Prevention – Strict guardrails against fabrication and misinformation
• Beautiful UI – Modern, responsive chat interface
• Production-Ready – Scalable backend with proper error handling
• Health Monitoring – Built-in health checks and uptime tracking

📋 Requirements

• Node.js 16+
• npm or yarn
• OpenAI API key (for production use)

🛠️ Installation

# Install dependencies
npm install

# Create .env file
cat > .env << EOF
PORT=3000
OPENAI_API_KEY=your_key_here
EOF

# Start the server
npm run dev

📁 Project Structure

├── server.js           # Express server with chat API
├── public/
│   └── index.html      # Chat UI
├── system_prompt.txt   # System prompt for the chatbot
└── package.json        # Dependencies

🔌 API Endpoints

POST /api/chat

Send a message and get a response.

Request:

{
  "message": "What's new on the website?",
  "conversationHistory": []
}

Response:

{
  "success": true,
  "message": "Response text...",
  "context": {
    "requiresLiveData": true,
    "toolsUsed": ["fetchLiveData"],
    "timestamp": "2026-01-12T10:30:00Z"
  }
}

GET /api/health

Check server health.

Response:

{
  "status": "healthy",
  "timestamp": "2026-01-12T10:30:00Z",
  "uptime": 3600
}

GET /api/system-prompt

Retrieve the system prompt (for debugging).

🎯 How It Works

1. User sends a message via the chat UI
2. Server analyzes if live data is needed (time-sensitive, external sources)
3. MCP tools are invoked if necessary to fetch real-time data
4. Response is generated using the system prompt guidelines
5. Assistant responds with proper citations and source attribution

🔐 Security Features

• ✅ No system prompt exposure to users
• ✅ Input validation and sanitization
• ✅ Rate limiting ready (add middleware as needed)
• ✅ Error handling without leaking internal details
• ✅ CORS headers (add if deploying to production)

🌐 Deployment

Option 1: Vercel (Recommended)

npm install -g vercel
vercel

Option 2: Heroku

heroku create your-app-name
git push heroku main

Option 3: Docker

Create a Dockerfile:

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
EXPOSE 3000
CMD ["npm", "start"]

🎨 Customization

Update Website Info

Edit server.js and update the system prompt or knowledge base.

Change UI Theme

Modify the CSS in public/index.html gradient colors and styling.

Add Real API Integration

Replace mock MCP tools in server.js with real OpenAI/Claude API calls.

📝 System Prompt Highlights

• Live-first philosophy – Prioritizes current data over static knowledge
• Hallucination prevention – Refuses to guess or invent information
• Transparent reasoning – Cites sources and explains reasoning
• Professional tone – Clear, concise, helpful communication
• Safety guardrails – Rejects prompt injection and abuse

🚦 Next Steps for Production

1. Integrate OpenAI/Claude API – Replace mock responses
2. Add MCP server – Real connection to external tools
3. Set up database – Store conversations and user data securely
4. Add authentication – Protect sensitive endpoints
5. Configure CORS – Allow cross-origin requests from your domain
6. Enable logging – Monitor and debug in production
7. Add rate limiting – Prevent abuse and control costs

📧 Support

For questions or issues, contact the site owner at srinivasanramanujam.sbs

📄 License

MIT License – See LICENSE file for details