Agentic Commerce MCP Demo (Goose + MCP UI)

A small Model Context Protocol (MCP) server that showcases agentic commerce UX using MCP UI blocks inside Goose. It returns rich, interactive HTML UI for a simple “find restaurants → view menu → fake order → receipt” flow.

Important notes:

• Demo only. No real sellers, data, payments, or money movement. Everything is synthetic or mocked.
• Not production code. This exists to demonstrate how MCP UI can drive a click-first agent experience.

Features

• Streamable HTTP MCP server with multiple tools:
  • find_restaurants – search nearby synthetic restaurants by city/state and query
  • view_restaurant – details card for a restaurant
  • view_menu – menu with images and pricing (mock catalog if “Square” is detected; otherwise a generic menu fallback)
  • order_takeout – interactive order page (edit quantities, remove items)
  • view_receipt – playful, fake receipt
• Click-first MCP UI: UI dispatches tool calls back to the agent on user actions.
• Dev HTML previews for local testing at http://127.0.0.1:8000/dev
• Large, synthetic dataset you can regenerate via scripts

What this is not

• No live Square API calls, no money movement, no real sellers or PII
• No persistent storage; no auth; no production hardening

Repo layout

• src/server.ts – MCP server with tools that render UI
• src/ui/* – HTML shell, styles, and view builders
• src/lib/restaurants.ts – local search over synthetic sellers + geocoding via OpenStreetMap Nominatim
• src/lib/square.ts – tiny mock for “Square detection” and sample catalogs
• src/data/* – generated JSON for restaurants and category menus
• src/scripts/* – generators for the data above
• scenarios.md – example conversational flows and UX notes

Prerequisites

• Node.js 20+
• pnpm (bundled with Node) or ppnpm/yarn

Setup

1. Install dependencies

pnpm install

2. Generate demo data (optional; the repo includes prebuilt JSON)

# Regenerate synthetic restaurants (5MB+ file)
# You can control density:
#   GEN_MIN_PER_CATEGORY=3 GEN_MAX_PER_CATEGORY=5 pnpm run generate:data
pnpm run generate:data

# Regenerate generic menus by category
pnpm run generate:menus

3. Run the MCP server (dev)

# Starts an HTTP (streamable) MCP server on 127.0.0.1:8000/mcp
pnpm run dev
# or
pnpm run dev:mcp

Environment variables you can set:

• MCP_HOST (default: 127.0.0.1)
• MCP_PORT (default: 8000)
• MCP_GEOCODE_USERAGENT (default: "mcp-agentic-commerce/1.0 (+https://squareup.com)")

4. Try the local UI previews in a browser

• http://127.0.0.1:8000/dev
• Example: http://127.0.0.1:8000/dev/restaurants?city=Austin&state=TX&query=bbq

Use with Goose

This project is designed to be consumed as an MCP extension by Goose.

Option A — Add manually in Goose settings:

• Open Goose Desktop → Settings → Extensions → Add MCP server
• Type: HTTP (streamable)
• URL: http://127.0.0.1:8000/mcp
• Name: Agentic Commerce MCP Demo
• Save. Start a new chat and ask something like:
  • “Find coffee around Austin”
  • “Show pizza near Toronto”
  • “Order two lattes from Midtown Bean at 9:15 under Sam.”

Option B — Use the MCP Inspector (handy for testing tools and UI):

# Runs the Inspector against this server
pnpm run dev:inspector
# Then open the printed Inspector URL; try executing tools directly

Tip: If your model/agent supports MCP UI, it will render the HTML cards, menus, and receipts inline and dispatch tool calls on button clicks.

Tool reference

• find_restaurants
  • args: city (string, default "Austin"), state (optional), query (optional), limit (1..25, default 10)
  • returns: UI list of nearby sellers; buttons to “Details” and “Order Now”
• view_restaurant
  • args: business_id (string)
  • returns: UI card with address, hours, phone, website; CTA buttons
• view_menu
  • args: business_id (string)
  • behavior: if mock "Square" is detected -> use mock catalog; else generic menu by primary category
• order_takeout
  • args: business_id (string), items (array of { name, qty, price })
  • returns: interactive order table with totals and a Place Order button
• view_receipt
  • args: business_id (string), items (same as above)
  • returns: playful demo receipt UI

Data notes

• Restaurants are synthetic and based on seeded generators across many US/CA cities. You can regenerate or reduce the dataset density via env vars on the generator script.
• Menu images are hotlinked from Unsplash and used only for illustrative purposes in this demo.

Safety and disclaimers

• For demonstration only; do not treat any information as factual.
• No money movement occurs. The “Place order” flow only renders a confirmation UI.

Deployment

Deploy to Netlify

This project is configured to deploy as a serverless function on Netlify:

1. Connect to Netlify:

   • Go to Netlify
   • Click "Add new site" → "Import an existing project"
   • Connect your GitHub repository

2. Build Settings (auto-detected from netlify.toml):

   • Build command: pnpm run build
   • Publish directory: dist

3. Deploy:

   • Netlify will automatically deploy on push to main
   • Your MCP server will be available at: https://your-site-name.netlify.app/mcp
   • Dev preview available at: https://your-site-name.netlify.app/dev

4. Use with Goose (Production):

   • Once deployed, use your Netlify URL in Goose settings
   • Type: HTTP (streamable)
   • URL: https://your-site-name.netlify.app/mcp

License

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