AcmeShop MCP AI Summit workshop

A small, local demonstration of an AI agent using MCP to investigate AcmeShop operational data and create an incident. The MCP server wraps a mock REST API; the API alone reads and writes JSON files.

User → AI client → MCP → MCP server → Mock REST API → JSON data

Prerequisites

Node.js 20 or later (Node 22 LTS recommended) and npm. No credentials or external services are needed.

Related MCP server: MCP API Tool Demo

Install and run

npm install
npm run dev

This starts the API at http://localhost:3000 and starts the MCP process over stdio. Open the API URL for the tiny live dashboard. To run them separately, use npm run api and, in another terminal, npm run mcp.

Reset the workshop after a write demo:

npm run reset

Test the REST API

curl http://localhost:3000/customers/C001
curl 'http://localhost:3000/deployments?service=checkout'
curl http://localhost:3000/incidents
curl -X POST http://localhost:3000/incidents -H 'content-type: application/json' -d '{"service":"checkout","severity":"SEV-2","title":"Demo incident","description":"Created through the mock REST API","assignedTo":"E-42"}'

Connect an MCP client

The server uses the standard local stdio transport. Start the mock API first, then configure a local MCP-capable client with this command, from the repository directory:

{
  "mcpServers": {
    "acmeshop": {
      "command": "npm",
      "args": ["run", "mcp"],
      "cwd": "/Users/prateeksharma/Documents/ChatGPT/Confrence"
    }
  }
}

For ChatGPT specifically, local stdio processes and localhost are not automatically reachable from every ChatGPT product or deployment. Use the local Codex desktop MCP configuration if available, or expose the server through a supported remote MCP transport only after adding appropriate authentication. This workshop intentionally keeps the default configuration local and credential-free.

The MCP process forwards every tool call to http://localhost:3000; it never reads the data files directly. The eight tools are intentionally independent so the model chooses how to investigate.

See the workshop guide and presenter script.