Skip to main content
Glama

E-commerce Local MCP Server

by AnisurRahman06046
GitHub
Python
OverviewInspectNewEndpointsSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

E-commerce Local MCP Server

A local Model Context Protocol (MCP) server that integrates with e-commerce platforms, enabling shop owners to query their business data using natural language through AI models running on local infrastructure.

Overview

This project provides a secure, privacy-focused solution for e-commerce platforms to offer AI-powered data queries without sending sensitive business data to external services. The server leverages multiple local AI models (Llama 3, Mistral, Phi-3) to process natural language queries and retrieve data from e-commerce databases.

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

# Clone the repository git clone <repository-url> cd test # Install dependencies pip install -r requirements.txt # Configure environment cp .env.example .env # Edit .env with your database credentials and API keys # Initialize database python scripts/init_db.py # Download AI models python scripts/download_models.py # Start the server python main.py

Configuration

Edit the .env file with your specific configuration:

DATABASE_URL=postgresql://user:password@localhost/ecommerce_db REDIS_URL=redis://localhost:6379 PLATFORM_AUTH_URL=https://your-platform.com/auth GPU_MEMORY_LIMIT=8192 MODEL_CACHE_DIR=./models

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

# Run unit tests pytest tests/unit/ # Run integration tests pytest tests/integration/ # Run performance tests pytest tests/performance/ # Generate coverage report pytest --cov=src tests/

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

# Build the image docker build -t ecommerce-mcp-server . # Run the container docker run -d \ --gpus all \ -p 8000:8000 \ -v ./data:/app/data \ -v ./models:/app/models \ --env-file .env \ ecommerce-mcp-server

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.

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Enables e-commerce shop owners to query their business data using natural language through local AI models. Provides secure, privacy-focused access to sales reports, inventory management, customer analytics, and order data without sending sensitive information to external services.

  1. Overview
    1. Key Features
      1. Target Users
        1. Architecture
          1. Core Components
          2. Technology Stack
        2. Performance Targets
          1. Development Phases
            1. Phase 1: Foundation Setup (Weeks 1-2)
            2. Phase 2: Core Functionality (Weeks 3-5)
            3. Phase 3: Integration & Testing (Weeks 6-7)
            4. Phase 4: Deployment & Documentation (Week 8)
          2. Security Features
            1. Quick Start
              1. Prerequisites
              2. Installation
              3. Configuration
            2. Usage Examples
              1. Basic Queries
              2. Advanced Analytics
            3. API Endpoints
              1. Chat Interface
              2. Model Management
              3. Tools
            4. Available Tools
              1. Monitoring and Maintenance
                1. Health Checks
                2. Logging
              2. Contributing
                1. Testing
                  1. Deployment
                    1. Production Deployment
                    2. Docker Deployment
                  2. Support
                    1. License
                      1. Changelog

                        New MCP Servers

                        MCP directory API

                        We provide all the information about MCP servers via our MCP API.

                        curl -X GET 'https://glama.ai/api/mcp/v1/servers/AnisurRahman06046/mcptestwithmodel'

                        If you have feedback or need assistance with the MCP directory API, please join our Discord server