# 🤖 AI Customer Support Bot - MCP Server

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![Python](https://img.shields.io/badge/python-v3.8+-blue.svg)
![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat&logo=fastapi)
![PostgreSQL](https://img.shields.io/badge/PostgreSQL-316192?style=flat&logo=postgresql&logoColor=white)
![MCP](https://img.shields.io/badge/MCP-Protocol-green)
![License](https://img.shields.io/badge/license-MIT-blue.svg)

*A modern, extensible MCP server framework for building AI-powered customer support systems*


[Features](#-features) • [Quick Start](#-quick-start) • [API Reference](#-api-reference) • [Architecture](#-architecture) • [Contributing](#-contributing)

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---

## 🌟 Overview

A **Model Context Protocol (MCP)** compliant server framework built with modern Python. Designed for developers who want to create intelligent customer support systems without vendor lock-in. Clean architecture, battle-tested patterns, and ready for any AI provider.

```mermaid
graph TB
    Client[HTTP Client] --> API[API Server]
    API --> MW[Middleware Layer]
    MW --> SVC[Service Layer]
    SVC --> CTX[Context Manager]
    SVC --> AI[AI Integration]
    SVC --> DAL[Data Access Layer]
    DAL --> DB[(PostgreSQL)]
```

## ✨ Features

<table>
<tr>
<td>

🏗️ **Clean Architecture**  
Layered design with clear separation of concerns

📡 **MCP Compliant**  
Full Model Context Protocol implementation

</td>
<td>

🔒 **Production Ready**  
Auth, rate limiting, monitoring included

🚀 **High Performance**  
Built on FastAPI with async support

</td>
</tr>
<tr>
<td>

🔌 **AI Agnostic**  
Integrate any AI provider easily

📊 **Health Monitoring**  
Comprehensive metrics and diagnostics

</td>
<td>

🛡️ **Secure by Default**  
Token auth and input validation

📦 **Batch Processing**  
Handle multiple queries efficiently

</td>
</tr>
</table>

## 🚀 Quick Start

### Prerequisites

- Python 3.8+
- PostgreSQL
- Your favorite AI service (OpenAI, Anthropic, etc.)

### Installation

```bash
# Clone and setup
git clone https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server.git
cd AI-Customer-Support-Bot--MCP-Server

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Setup environment
cp .env.example .env
# Edit .env with your configuration
```

### Configuration

```bash
# .env file
DATABASE_URL=postgresql://user:password@localhost/customer_support_bot
SECRET_KEY=your-super-secret-key
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_PERIOD=60
```

### Run

```bash
# Setup database
createdb customer_support_bot

# Start server
python app.py
# 🚀 Server running at http://localhost:8000
```

## 📡 API Reference

<details>
<summary><strong>Core Endpoints</strong></summary>

### Health Check
```http
GET /mcp/health
```

### Process Single Query
```http
POST /mcp/process
Content-Type: application/json
X-MCP-Auth: your-token
X-MCP-Version: 1.0

{
  "query": "How do I reset my password?",
  "priority": "high"
}
```

### Batch Processing
```http
POST /mcp/batch
Content-Type: application/json
X-MCP-Auth: your-token

{
  "queries": [
    "How do I reset my password?",
    "What are your business hours?"
  ]
}
```

</details>

<details>
<summary><strong>Response Format</strong></summary>

### Success Response
```json
{
  "status": "success",
  "data": {
    "response": "Generated AI response",
    "confidence": 0.95,
    "processing_time": "120ms"
  },
  "meta": {
    "request_id": "req_123456",
    "timestamp": "2024-02-14T12:00:00Z"
  }
}
```

### Error Response
```json
{
  "code": "RATE_LIMIT_EXCEEDED",
  "message": "Rate limit exceeded",
  "details": {
    "retry_after": 60,
    "timestamp": "2024-02-14T12:00:00Z"
  }
}
```

</details>

## 🏗️ Architecture

### Project Structure
```
📦 AI-Customer-Support-Bot--MCP-Server
├── 🚀 app.py              # FastAPI application
├── 🗄️  database.py         # Database configuration
├── 🛡️  middleware.py       # Auth & rate limiting
├── 📋 models.py          # ORM models
├── ⚙️  mcp_config.py      # MCP protocol config
├── 📄 requirements.txt   # Dependencies
└── 📝 .env.example      # Environment template
```

### Layer Responsibilities

| Layer | Purpose | Components |
|-------|---------|------------|
| **API** | HTTP endpoints, validation | FastAPI routes, Pydantic models |
| **Middleware** | Auth, rate limiting, logging | Token validation, request throttling |
| **Service** | Business logic, AI integration | Context management, AI orchestration |
| **Data** | Persistence, models | PostgreSQL, SQLAlchemy ORM |

## 🔌 Extending with AI Services

### Add Your AI Provider

1. **Install your AI SDK:**
```bash
pip install openai  # or anthropic, cohere, etc.
```

2. **Configure environment:**
```bash
# Add to .env
AI_SERVICE_API_KEY=sk-your-api-key
AI_SERVICE_MODEL=gpt-4
```

3. **Implement service integration:**
```python
# In service layer
class AIService:
    async def generate_response(self, query: str, context: dict) -> str:
        # Your AI integration here
        return ai_response
```

## 🔧 Development

### Running Tests
```bash
pytest tests/
```

### Code Quality
```bash
# Format code
black .

# Lint
flake8

# Type checking
mypy .
```

### Docker Support
```dockerfile
# Coming soon - Docker containerization
```

## 📊 Monitoring & Observability

### Health Metrics
- ✅ Service uptime
- 🔗 Database connectivity
- 📈 Request rates
- ⏱️ Response times
- 💾 Memory usage

### Logging
```python
# Structured logging included
{
  "timestamp": "2024-02-14T12:00:00Z",
  "level": "INFO",
  "message": "Query processed",
  "request_id": "req_123456",
  "processing_time": 120
}
```

## 🔒 Security

### Built-in Security Features
- 🔐 **Token Authentication** - Secure API access
- 🛡️ **Rate Limiting** - DoS protection
- ✅ **Input Validation** - SQL injection prevention
- 📝 **Audit Logging** - Request tracking
- 🔒 **Environment Secrets** - Secure config management

## 🚀 Deployment

### Environment Setup
```bash
# Production environment variables
DATABASE_URL=postgresql://prod-user:password@prod-host/db
RATE_LIMIT_REQUESTS=1000
LOG_LEVEL=WARNING
```

### Scaling Considerations
- Use connection pooling for database
- Implement Redis for rate limiting in multi-instance setups
- Add load balancer for high availability
- Monitor with Prometheus/Grafana

## 🤝 Contributing

We love contributions! Here's how to get started:

### Development Setup
```bash
# Fork the repo, then:
git clone https://github.com/your-username/AI-Customer-Support-Bot--MCP-Server.git
cd AI-Customer-Support-Bot--MCP-Server

# Create feature branch
git checkout -b feature/amazing-feature

# Make your changes
# ...

# Test your changes
pytest

# Submit PR
```

### Contribution Guidelines
- 📝 Write tests for new features
- 📚 Update documentation
- 🎨 Follow existing code style
- ✅ Ensure CI passes

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/chiragpatankar-ai-customer-support-bot-mcp-server-badge.png)](https://mseep.ai/app/chiragpatankar-ai-customer-support-bot-mcp-server)

## 📄 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

---

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**Built with ❤️ by [Chirag Patankar](https://github.com/ChiragPatankar)**

⭐ **Star this repo if you find it helpful!** ⭐

[Report Bug](https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server/issues) • [Request Feature](https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server/issues) • [Documentation](https://github.com/ChiragPatankar/AI-Customer-Support-Bot--MCP-Server/wiki)

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