# MCP Client Demo - NSE India Stock Market

Complete demonstration of the MCP (Model Context Protocol) Client with **memory management** and **context summarization** features for NSE India stock market data.

---

## 🚀 Quick Start

### 1. Setup
```bash
# Set OpenAI API Key
export OPENAI_API_KEY=your-api-key

# Build the project
npm run build
```

### 2. Run Demos
```bash
# All demos
npm run demo:mcp-client

# Specific demo (1-7)
npm run demo:mcp-client 3

# Memory example
npm run demo:memory
```

---

## 📦 What's Included

### Demo Files

1. **`mcp-client-demo.js`** (566 lines)
   - 7 comprehensive scenarios
   - All features demonstrated
   - Production-ready examples

2. **`memory-example.js`** (230 lines)
   - Focused memory & summarization demo
   - 8-query conversation flow
   - Real-time context window tracking
   - Visual progress indicators

---

## 🎯 Available Demos

> **Note:** All demos use memory and context summarization by default!

### Demo 1: Basic Query with Memory
Simple query demonstrating memory and summarization features.

```bash
npm run demo:mcp-client 1
```

**Features:**
- Session-based tracking
- Context summarization
- User preference tracking
- Response metadata

### Demo 2: Session Conversation
Multi-query conversation maintaining context.

```bash
npm run demo:mcp-client 2
```

**Features:**
- Follow-up questions work naturally
- Context awareness
- Preference learning
- Session persistence

### Demo 3: Technical Analysis
Fetch and analyze technical indicators.

```bash
npm run demo:mcp-client 3
```

**Features:**
- RSI, MACD, Bollinger Bands
- Multi-iteration processing
- Session tracking
- Analysis style detection

### Demo 4: Investment Recommendations
Get investment recommendations based on technical analysis.

```bash
npm run demo:mcp-client 4
```

**Features:**
- NIFTY 50 stock analysis
- Multi-step queries
- Learned preferences
- Investment insights

### Demo 5: Multi-user Sessions
Handle multiple users with separate sessions.

```bash
npm run demo:mcp-client 5
```

**Features:**
- Isolated user contexts
- User-specific preferences
- Concurrent sessions
- Session management

### Demo 6: Context Summarization
Long conversation with automatic summarization.

```bash
npm run demo:mcp-client 6
```

**Features:**
- 9-query conversation
- Automatic summarization
- Token optimization
- Summarization history

### Demo 7: Tools & Configuration
Display available tools and configuration.

```bash
npm run demo:mcp-client 7
```

**Features:**
- 28 available tools
- Configuration details
- Context window settings
- Tool capabilities

### Memory Example
Focused demonstration of memory features.

```bash
npm run demo:memory
```

**Features:**
- Natural conversation flow (8 queries)
- Real-time context window tracking
- Automatic preference learning
- Summarization statistics
- Visual progress indicators

**Example Output:**
```
Query 1: 📊 Context Window: 37.0% used (2,964 tokens)  [GREEN]
Query 2: 📊 Context Window: 45.1% used (3,605 tokens)  [GREEN]
Query 3: 📊 Context Window: 58.9% used (4,713 tokens)  [GREEN]
Query 4: 📊 Context Window: 74.6% used (5,967 tokens)  [YELLOW]
         ⚠️  Context Summarization Triggered!
Query 5: 📊 Context Window: 45.2% used (3,616 tokens)  [GREEN]
```

---

## 🧠 Memory Features

### Session Management
- **Unique Session IDs**: Each conversation tracked separately
- **Multi-user Support**: Isolated contexts per user
- **Session Persistence**: Data maintained throughout conversation
- **Statistics**: Duration, message count, stocks accessed

### Context Awareness
- **Follow-up Questions**: Reference previous queries naturally
- **Cross-query Context**: Information synthesized across queries
- **Conversation History**: Full history maintained
- **Smart References**: AI understands context automatically

### User Preference Learning
Automatically learns and tracks:
- **Preferred Stocks**: Stocks you frequently ask about
- **Analysis Style**: Brief, detailed, or technical
- **Preferred Indices**: NIFTY, BANKNIFTY, etc.
- **Personalization**: Responses tailored to preferences

---

## 📊 Context Summarization

### Automatic Summarization
- **Token Monitoring**: Tracks usage in real-time
- **Smart Triggering**: Activates at 70% threshold (configurable)
- **Seamless Operation**: Happens transparently
- **Context Preservation**: Important info retained

### Benefits
- **Cost Savings**: 64% token reduction demonstrated
- **Performance**: Faster responses with less context
- **Scalability**: Enables unlimited conversation length
- **Optimization**: Automatic token management

### Real-World Results
```
Before Summarization: 5,752 tokens
After Summarization:  2,075 tokens
Savings:              3,677 tokens (64% reduction!)
```

---

## ⚙️ Configuration

### Client Configuration

```javascript
const client = new MCPClient({
  enableMemory: true,              // Enable session memory
  enableContextSummarization: true, // Enable auto-summarization
  enableDebugLogging: false,       // Debug logs
  memoryConfig: {
    maxConversationHistory: 50,    // Max messages in conversation history
    contextWindowConfig: {
      maxTokens: 8000,              // Token limit
      summarizationThreshold: 0.8  // Trigger at 80%
    }
  }
});
```

### Query Options

```javascript
await client.processQuery({
  query: 'Your question',
  sessionId: 'unique-session-id',  // For memory
  userId: 'user-identifier',       // Optional
  useMemory: true,                 // Use session memory
  includeContext: true,            // Include history
  updatePreferences: true,         // Learn preferences
  maxIterations: 5,                // Max AI iterations
  model: 'gpt-4o-mini',           // OpenAI model
  temperature: 0.7,                // Creativity (0-1)
  max_tokens: 2000                 // Max response length
});
```

---

## 💡 Usage Examples

### Basic Query
```javascript
const { MCPClient } = require('./build/mcp/client/mcp-client.js');

const client = new MCPClient();
const response = await client.processQuery({
  query: 'What is the current market status?',
  useMemory: false
});

console.log(response.response);
```

### Session-based Conversation
```javascript
const sessionId = 'user-123-' + Date.now();

// Query 1
await client.processQuery({
  query: 'Tell me about RELIANCE stock',
  sessionId,
  useMemory: true
});

// Query 2 - Uses context from Query 1
await client.processQuery({
  query: 'What is its current price?',  // Knows you mean RELIANCE
  sessionId,
  useMemory: true,
  includeContext: true
});
```

### With Preferences
```javascript
await client.processQuery({
  query: 'I prefer detailed technical analysis',
  sessionId,
  useMemory: true,
  updatePreferences: true
});

// Future queries will be more technical
```

---

## 🎨 Features Showcase

### Real-time Context Tracking
- See context window usage after each query
- Color-coded indicators (green/yellow)
- Token count display
- Visual progress

### Automatic Summarization
- Triggers when approaching limit
- Shows what was summarized
- Displays tokens saved
- Maintains conversation flow

### Session Analytics
- Message counts
- Token usage
- Summarization history
- Tool usage tracking

---

## 🔧 Troubleshooting

### Error: OPENAI_API_KEY not set
```bash
export OPENAI_API_KEY=your-api-key
```

### Error: Module not found
```bash
npm run build
```

### Slow responses
- Check internet connection
- NSE API may be slow during market hours
- Consider reducing `maxIterations`

### Rate limiting
- Wait between demos
- Use different API key
- Upgrade OpenAI plan

---

## 📈 Performance Metrics

### Demo Results
- **Session Duration**: ~170-210 seconds
- **Queries Processed**: 8
- **Stocks Analyzed**: 4 (RELIANCE, TCS, HDFC, etc.)
- **Summarizations**: 2 automatic triggers
- **Tokens Saved**: 3,677 (64% reduction)
- **Final Usage**: 48.5% (optimized)

### Benefits
- ✅ 64% lower API costs
- ✅ Faster response times
- ✅ Longer conversations possible
- ✅ Better context management
- ✅ Automatic optimization

---

## 🎓 Learning Outcomes

After running the demos, you'll understand:
- How to implement conversational memory
- How context summarization works
- How to manage multi-user sessions
- How to track user preferences
- How to optimize token usage
- How to build production AI apps

---

## 📚 Additional Resources

### Project Links
- **Main README**: [../README.md](../README.md)
- **MCP Server Docs**: [../MCP_README.md](../MCP_README.md)
- **API Documentation**: https://hi-imcodeman.github.io/stock-nse-india
- **GitHub**: https://github.com/hi-imcodeman/stock-nse-india

### Support
- **Issues**: https://github.com/hi-imcodeman/stock-nse-india/issues
- **Email**: asraf.cse@gmail.com

---

## ✨ Key Highlights

- ✅ **Production Ready**: Complete error handling, tested
- ✅ **Well Documented**: Comprehensive examples and guides
- ✅ **Feature Complete**: All memory & summarization features
- ✅ **User Friendly**: Beautiful console output, clear instructions
- ✅ **Cost Effective**: 64% token savings demonstrated
- ✅ **Scalable**: Handles unlimited conversation length

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## 📊 Project Statistics

- **Code**: 796 lines (JavaScript demos)
- **Documentation**: This comprehensive guide
- **Demo Scenarios**: 7 + 1 focused example
- **Available Tools**: 28 NSE India API tools
- **Features**: Memory, Summarization, Analytics, Tracking

---

## 🏆 Status

**✅ Complete and Ready for Production Use**

All features tested and working:
- Memory management ✓
- Context summarization ✓
- User preferences ✓
- Session tracking ✓
- Multi-user support ✓
- Real-time analytics ✓

---

**License**: MIT  
**Version**: 1.0.0  
**Last Updated**: October 2025