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
- User sends a message via the chat UI
- Server analyzes if live data is needed (time-sensitive, external sources)
- MCP tools are invoked if necessary to fetch real-time data
- Response is generated using the system prompt guidelines
- 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
- Integrate OpenAI/Claude API – Replace mock responses
- Add MCP server – Real connection to external tools
- Set up database – Store conversations and user data securely
- Add authentication – Protect sensitive endpoints
- Configure CORS – Allow cross-origin requests from your domain
- Enable logging – Monitor and debug in production
- 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