E-commerce MongoDB MCP Server

A modern Model Context Protocol (MCP) server that integrates with e-commerce platforms, enabling shop owners to query their business data using natural language. Built with MongoDB Atlas, clean architecture, and async Python.

🚀 Overview

This project provides a scalable, cloud-native solution for e-commerce platforms to offer AI-powered data queries. The server uses MongoDB Atlas for flexible data storage and supports multiple local AI models (Llama 3, Mistral, Phi-3) to process natural language queries.

🏗️ Modern Architecture

• MongoDB Atlas: Cloud-native document database with connection pooling

• AsyncMongoClient: Modern async database operations

• Clean Architecture: Separated concerns and dependency injection

• UUID Primary Keys: Scalable, database-agnostic document IDs

• Type-Safe Models: Pydantic validation with full async support

Key Features

• Natural Language Queries: Shop owners can ask questions about their business data in plain English

• Local AI Processing: All AI inference happens locally for data privacy and cost efficiency

• Multi-Model Support: Dynamic selection between Llama 3, Mistral, and Phi-3 based on query complexity

• Secure Data Access: Role-based permissions with secure authentication integration

• Real-time Responses: Low-latency query processing for seamless user experience

• Comprehensive Tools: Built-in tools for sales reports, inventory checks, customer analytics, and order management

Target Users

• E-commerce platform shop owners

• Platform administrators

• Customer support teams

Architecture

Core Components

1. MCP Server Core: Central request processing hub with FastAPI

2. Model Management System: Dynamic AI model lifecycle management

3. Tool Registry: Extensible data operation definitions

4. Data Access Layer: Secure database interface with permission enforcement

5. Integration Layer: Platform authentication and API gateway

Technology Stack

• Backend: Python, FastAPI, SQLAlchemy

• AI Inference: llama.cpp, Ollama, CUDA

• Database: PostgreSQL

• Caching: Redis

• Authentication: JWT integration with existing platform

Performance Targets

• Simple queries: < 3 seconds response time

• Complex queries: < 10 seconds response time

• System uptime: > 99.5%

• Concurrent users: 10+ without degradation

• Query accuracy: 90%+ for data retrieval

Development Phases

Phase 1: Foundation Setup (Weeks 1-2)

• Infrastructure preparation with GPU support

• MCP server core implementation

• Security framework establishment

Phase 2: Core Functionality (Weeks 3-5)

• Model management system

• Tool registry development

• Query processing pipeline

Phase 3: Integration & Testing (Weeks 6-7)

• API integration with chat UI

• Comprehensive testing suite

• Performance optimization

Phase 4: Deployment & Documentation (Week 8)

• Production deployment

• Documentation and training

• Operational handover

Security Features

• Token-based authentication validation

• Role-based access control

• Input sanitization and injection prevention

• Comprehensive audit logging

• Local data processing (no external API calls)

• Encrypted data transmission

Quick Start

Prerequisites

• Python 3.9+

• CUDA-capable GPU (recommended)

• PostgreSQL database

• Redis server

• Access to e-commerce platform database

Installation

Configuration

Edit the .env file with your specific configuration:

Usage Examples

Basic Queries

• "What were my sales last week?"

• "Show me low inventory items"

• "How many orders are pending?"

• "Which products are my top sellers this month?"

Advanced Analytics

• "Compare this month's revenue to last month"

• "Show customer acquisition trends"

• "Analyze seasonal sales patterns"

• "Generate inventory turnover report"

API Endpoints

Chat Interface

• POST /chat/query - Process natural language query

• GET /chat/history/{session_id} - Retrieve conversation history

• POST /chat/session - Create new chat session

Model Management

• GET /models/status - Check model availability

• POST /models/load/{model_name} - Load specific model

• DELETE /models/unload/{model_name} - Unload model

Tools

• GET /tools/list - Available data tools

• POST /tools/execute - Execute specific tool

Available Tools

1. Sales Analytics: Revenue reports, trend analysis, period comparisons

2. Inventory Management: Stock levels, low inventory alerts, reorder suggestions

3. Customer Insights: Customer analytics, segmentation, behavior patterns

4. Order Management: Order status, fulfillment tracking, shipping analytics

5. Product Performance: Best sellers, product analytics, category insights

Monitoring and Maintenance

Health Checks

• GET /health - System health status

• GET /metrics - Performance metrics

• GET /models/health - AI model status

Logging

• Application logs: logs/app.log

• Query logs: logs/queries.log

• Security audit: logs/security.log

Contributing

1. Fork the repository

2. Create a feature branch (git checkout -b feature/new-feature)

3. Commit your changes (git commit -am 'Add new feature')

4. Push to the branch (git push origin feature/new-feature)

5. Create a Pull Request

Testing

Deployment

Production Deployment

1. Set up production environment with GPU support

2. Configure secure database connections

3. Set up SSL certificates

4. Configure monitoring and alerting

5. Deploy using Docker or direct installation

Docker Deployment

Support

For technical issues and feature requests, please:

1. Check the documentation

2. Search existing issues

3. Create a new issue with detailed description

4. Contact the development team

License

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

Changelog

See CHANGELOG.md for version history and updates.