Restaurant MCP Server

Connect AI assistants (Claude, etc.) to a realistic restaurant POS system through the Model Context Protocol.

Status: 🚧 Work in progress

What it does

Ask your AI assistant things like:

• "What were our best-selling items today?"

• "Which ingredients are running low?"

• "Add 2 Adana kebabs to table 5."

Related MCP server: Food Order Bot MCP Server

Architecture

Claude Desktop  ──MCP──▶  pos_mcp (MCP server)  ──HTTP──▶  pos_api (mock POS, FastAPI)  ──▶  SQLite

• src/pos_api — a mock restaurant POS REST API with realistic seed data

• src/pos_mcp — the MCP server exposing POS capabilities as tools, resources and prompts

Quick start

python -m venv .venv
.venv\Scripts\activate          # Windows
pip install -e ".[dev]"
python -m pos_api.db.seed --reset   # generate 90 days of realistic data
uvicorn pos_api.main:app --reload

Then open http://127.0.0.1:8000/docs

The data

A synthetic but realistic Turkish kebab restaurant (37 menu items, 15 tables, ~9,000 orders over 90 days). The seed data follows real-world patterns so analytical questions have meaningful answers:

• Lunch and dinner peaks, busier Fridays and weekends, a public-holiday spike (Oct 29)

• Seasonality: cold drinks fade, soups and tea rise as summer turns to autumn

• A menu-wide price increase ~45 days ago (historical order prices are preserved)

• A rising-star menu item and a top-performing waiter

• Voided items and cancelled orders with reasons

• A full stock ledger (sales, purchases, waste) and a supplier delay leaving some ingredients below their reorder level

Privacy by design: there is no customer table; no personal data is stored. Timestamps are timezone-aware (RESTAURANT_TZ, default Europe/Istanbul).

Roadmap

• Data model & seed data

• Mock POS API

• MCP server (read tools, write tools with confirmation)

• Tests & evals

• Docker

• Demo video

License

MIT