MCP E-Commerce Agent

MCP E-Commerce Agent

A proof of concept MCP server that gives AI agents business context for e-commerce operations including orders, inventory, logistics, returns, claims, and payments.

Category
Visit Server

README

MCP for E-Commerce — Business-Context AI Agents

Docs Deploy docs

A proof of concept that uses the Model Context Protocol (MCP) to give an e-commerce company's AI agents the business context they need to run operations — live orders, inventory, logistics, returns, claims, and payments — safely and at scale.

📖 Full documentation: https://yassineteimi.github.io/mcp-ecommerce-agent/

The idea

A general LLM doesn't know that order #88213 is late because its driver called in sick. MCP connects AI agents to real business systems through one open standard, turning a brittle N×M web of integrations into a reusable N+M model: wrap each system once as an MCP server, and any agent can use it.

The PoC follows a deliberate maturity path:

  1. Internal first — a supply-chain manager gets an assistant with governed, read-mostly access to every fulfilment system; the agent proposes actions, a human approves them.
  2. Mature — enable trusted write actions (reorder, reroute, approve return) behind guardrails, audit, and evaluation.
  3. Customer-facing — expose a curated, per-tenant-isolated subset (order tracking, returns) into the customer experience.

How it works

  • Servers, one per domain — Orders, Inventory, Logistics, Returns, Claims, Payments — each fronting a system of record with isolated credentials.
  • Three MCP primitives — Resources (read-only context), Tools (human-approved actions), Prompts (reusable operating procedures).
  • Built with the Python MCP SDK (FastMCP) — a complete server is ~15 lines; the same code runs locally over stdio or remotely over Streamable HTTP.
  • Secure by design — OAuth 2.1 authorization, role-based scoped access, per-server least privilege, transport hardening, and per-tenant isolation for the customer phase.

Documentation

Page Contents
Overview What MCP is and why it matters for AI-agent integration
Architecture Servers, clients, and the Tools/Resources/Prompts flow (with diagrams)
Implementation How the servers and clients are built
Security Authorization, scoped access, transport hardening

Docs are built with MkDocs Material and deployed to GitHub Pages by .github/workflows/ci.yml on every push to main.

Status

A design-led proof of concept: the architecture, code patterns, and security model are production-shaped and standards-accurate, illustrated against a representative e-commerce domain. Learn more about MCP at modelcontextprotocol.io.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured