Jam – Toast MCP Server 🍓
A Model Context Protocol (MCP) server for Toast POS — giving AI agents direct access to restaurant operations: menu management, orders, inventory, labor, delivery integrations, and smart operational insights.
Connect any MCP-compatible AI (Claude, GPT-4, Cursor, Continue, and others) to your Toast account and turn natural language into real POS actions — no dashboard, no manual lookups, no custom integration code.
This project was born out of a simple idea: restaurant owners deserve the same kind of intelligent assistant that enterprise businesses take for granted. Not a chatbot. Not a dashboard. Something that watches your inventory, knows your peak hours, and surfaces insights when you need them most.
We built this as the data layer for an AI co-pilot system. It exposes the Toast API as a clean set of MCP tools that any LLM can call — so instead of logging into Toast, checking stock levels, cross-referencing delivery platforms, and manually updating your menu, you just ask.
What It Does
This server wraps the Toast API into 50+ LLM-callable tools across every major area of restaurant operations:
| Domain | Capabilities |
|---|
| Inventory | Real-time stock levels, low-stock alerts, auto-menu adjustments when ingredients run out |
| Orders | Order history, details, void handling, third-party delivery filtering (UberEats, DoorDash, GrubHub, Postmates, Caviar) |
| Menu | Browse items, categories, pricing, search functionality |
| Labor | Employee management, shift tracking, labor cost visibility |
| Analytics | Revenue by period, peak hours, best-selling items, category breakdown |
| Financial | Daily/weekly/monthly summaries, tender breakdowns, payment method analysis |
| Operations | Open order tracking, void analysis, refund patterns, transaction monitoring |
| Retention | Frequent customer identification, lapsed customer detection, win-back messaging |
| Forecasting | Week-over-week trends, seasonal patterns, staffing demand signals |
| Smart Operations | Stock velocity predictions, peak hour detection, automated ordering recommendations |
Key difference: Unlike other Toast integrations, Jam includes native third-party delivery order tracking with platform-level revenue breakdown — something competitors haven't built.
Tools
Inventory
get_stock_levels: Full visibility into your ingredients.update_stock: Manual corrections after shipments.auto_86_item: Instant menu updates for depleted items.get_low_stock_items: Automated alerts for reordering.
Menu
get_menu: Comprehensive menu fetch.get_menu_item: Deep dive into specific selections.search_menu: Find what you need, fast.
Orders
get_orders: Monitor recent transactions.get_order_details: Audit specific orders.void_order: Handle corrections with ease.get_delivery_orders: Track third-party delivery orders (UberEats, DoorDash, GrubHub, Postmates, Caviar) with revenue breakdown by platform.
Labor & Staff
get_employees: Manage your team.get_time_entries: Track shifts and labor costs.
Smart Operations
analyze_stock_needs: Sales-velocity based predictions.detect_peak_hours: Staffing optimization intelligence.generate_wholesaler_list: Automated shopping list generation based on stock levels.
Getting Started
Prerequisites
Install via npm (recommended)
npx @dokdosolutions/toast-mcp
Or install from source
npm install
npm run build
Configure
cp .env.example .env
# Fill in your TOAST_CLIENT_ID, TOAST_CLIENT_SECRET, and TOAST_RESTAURANT_GUID
Run
npm start
Or connect it to your MCP host (like Claude Desktop) using the absolute path to the build:
{
"mcpServers": {
"jam": {
"command": "node",
"args": ["/absolute/path/to/toast-mcp/dist/index.js"],
"env": {
"TOAST_CLIENT_ID": "your_client_id",
"TOAST_CLIENT_SECRET": "your_client_secret",
"TOAST_RESTAURANT_GUID": "your_restaurant_guid"
}
}
}
}
Built By
Dokdo Solutions — AI integration for restaurant owners.
License
MIT
