Which integrations are available for this server?

Used for configuration management to securely store API keys, database connection strings, and other environment-specific settings. Used for version control and deployment of the MCP server codebase. Used as the database backend for storing user interactions and tracking data for the AI customer support system. Primary programming language used to build the MCP server application and its components.

🤖 AI Customer Support Bot - MCP Server

Python FastAPI PostgreSQL MCP License

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

Features • Quick Start • API Reference • Architecture • Contributing

🌟 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.

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)]

Related MCP server: MCP Starter

✨ Features

🏗️ Clean Architecture
Layered design with clear separation of concerns

📡 MCP Compliant
Full Model Context Protocol implementation

🔒 Production Ready
Auth, rate limiting, monitoring included

🚀 High Performance
Built on FastAPI with async support

🔌 AI Agnostic
Integrate any AI provider easily

📊 Health Monitoring
Comprehensive metrics and diagnostics

🛡️ Secure by Default
Token auth and input validation

📦 Batch Processing
Handle multiple queries efficiently

🚀 Quick Start

Prerequisites

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

Installation

# 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

# .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

# Setup database createdb customer_support_bot # Start server python app.py # 🚀 Server running at http://localhost:8000

📡 API Reference

Health Check

GET /mcp/health

Process Single Query

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

POST /mcp/batch Content-Type: application/json X-MCP-Auth: your-token { "queries": [ "How do I reset my password?", "What are your business hours?" ] }

Success Response

{ "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

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

🏗️ 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

Install your AI SDK:

pip install openai # or anthropic, cohere, etc.

Configure environment:

# Add to .env AI_SERVICE_API_KEY=sk-your-api-key AI_SERVICE_MODEL=gpt-4

Implement service integration:

# 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

pytest tests/

Code Quality

# Format code black . # Lint flake8 # Type checking mypy .

Docker Support

# Coming soon - Docker containerization

📊 Monitoring & Observability

Health Metrics

✅ Service uptime
🔗 Database connectivity
📈 Request rates
⏱️ Response times
💾 Memory usage

Logging

# 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

# 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

# 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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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AI Customer Support Bot - MCP Server