How does it provide data for testing?

The server generates mock product data on-demand and deterministically, eliminating the need for database seeding and ensuring frictionless testing and development.

How do AI clients interact with the server?

AI clients invoke the 28 MCP tools using Server-Sent Events (SSE) over HTTP, enabling real-time communication and interaction throughout the e-commerce lifecycle.

What specific e-commerce features does it offer?

It simulates a complete shopping journey: product search, cart, checkout, payment, order, shipment, reviews, support tickets, and wishlists across 5 categories: Grocery, Fashion, Electronics, Beauty, Home.

What is the Mock E-commerce Server?

It's a comprehensive backend simulation tool designed to integrate and test AI chatbots within a full e-commerce ecosystem, using 28 Model Context Protocol (MCP) tools.

What technologies power this mock server?

It's built with Java 21, Spring Boot 3.3.6, and Spring AI 1.0.0-M6, utilizing an in-memory H2 database for easy local setup and development.

Mock E-commerce Server

Name: Mock E-commerce Server
Author: arpanm

byarpanm

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Simulates a comprehensive e-commerce backend with 28 functional tools for AI chatbot integration across diverse product categories.

The Mock E-commerce Server provides a robust, general-purpose backend built with Java, Spring Boot, and Spring AI, designed specifically to integrate with AI chatbots. It offers a complete e-commerce ecosystem, covering everything from product search and cart management to checkout, payment, order fulfillment, and customer support. By exposing 28 Model Context Protocol (MCP) tools over HTTP/SSE, it enables AI clients like Claude, GPT, or Gemini to discover and invoke complex e-commerce operations across five major product categories: Grocery, Fashion, Electronics, Beauty, and Home, all while generating mock data on demand for frictionless development and testing.

主要功能

01Exposes 28 Model Context Protocol (MCP) tools for a full e-commerce lifecycle

02Supports 5 product categories: Grocery, Fashion, Electronics, Beauty, Home

030 GitHub stars

04Generates mock product data on-demand for frictionless testing

05Implements session-based authentication via phone number and platform secret

06Utilizes Server-Sent Events (SSE) for real-time tool invocation by AI clients

使用案例

01Developing and testing AI chatbots that require e-commerce interaction capabilities

02Demonstrating AI client functionalities within a simulated e-commerce environment

03Providing a configurable mock backend for e-commerce API integration testing