Claude, I’m hungry. 🍜

Order food from Uber Eats through natural language in Cursor or Claude Code — with an assistant that can browse menus, prep checkout, and (when you say so) place the order.

This project is an MCP server (Model Context Protocol): a small program your AI client starts over stdio so it can call tools like uber_eats_search and uber_eats_checkout_preview.
It is not a Cursor extension / VS Code plugin.

Under the hood: Uber web JSON APIs for search, menus, cart (add/remove via addItemsToDraftOrderV2 / removeItemsFromDraftOrderV2), checkout, and orders. Playwright is used for login, optional address picker UI, and place order fallback when API submit is unavailable or fails.

What you can do

• Browse & decide fast

  • Search and browse nearby stores

  • Pull full menus with categories and item prices

  • Get item detail payloads (including customization metadata where available)

• Cart & checkout prep

  • View cart via API (draft order + carts view)

  • Checkout preview with totals, fees, tip options, and delivery address

  • List eligible payment methods (when cart is non-empty)

  • Set checkout tip / select payment / apply promo / view savings

• Orders

  • Track active orders (and past orders)

  • “Reorder helper” that resolves the store + previous items (then add with uber_eats_add_to_cart API)

• Reverse-engineering mode

  • Run an API discovery browser that logs full request/response bodies to ~/.ubereats-api-log.jsonl

Quick start (after git clone)

From inside uber-eats-mcp/ (this folder):

macOS / Linux

chmod +x scripts/setup.sh
./scripts/setup.sh

Windows (PowerShell)

powershell -ExecutionPolicy Bypass -File scripts\setup.ps1

Manual (same as the scripts)

uv sync
uv run playwright install chromium

The setup script prints a ready-to-paste JSON block with the correct path to this clone.

Connect Cursor

1. Open Cursor → Settings → MCP (or edit the MCP config file your Cursor version uses).

2. Merge the uber-eats entry into your existing mcpServers object (do not delete other servers).

3. Restart Cursor or reload MCP.

If you prefer a project-local config, add .cursor/mcp.json in a project and paste the same mcpServers snippet.

Example shape (use the exact output from ./scripts/setup.sh so paths match your machine):

{
  "mcpServers": {
    "uber-eats": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/uber-eats-mcp", "uber-eats-mcp"]
    }
  }
}

Requires uv on your PATH: install uv.

Optional: “I’m hungry” Cursor rule

This repo includes .cursor/rules/uber-eats-hungry.mdc, which nudges the agent to call the Uber Eats tools when you sound hungry or want to order.

• It is not part of the pip / wheel install — pyproject.toml only packages the Python server (server.py, api.py, …). Cloning the repo (or copying the file) is what brings the rule in.

• For Cursor to load it, open uber-eats-mcp as the project root, or copy uber-eats-hungry.mdc into your own app’s .cursor/rules/.

Connect Claude Code

Add an .mcp.json at the root of the project you open in Claude Code (or use the global location your version documents). Use the same mcpServers JSON as above.

Typical flow (what the assistant should do)

1. uber_eats_login

2. uber_eats_get_preferences (optional personalization)

3. uber_eats_get_address (confirm delivery address)

4. uber_eats_search or uber_eats_nearby_restaurants

5. uber_eats_restaurant_menu → decide items

6. If needed: uber_eats_menu_item_detail / uber_eats_get_item_options

7. uber_eats_add_to_cart (API — requires restaurant_url)

8. uber_eats_view_cart

9. uber_eats_checkout_preview

10. Optional: uber_eats_list_payment_methods, uber_eats_set_checkout_payment, uber_eats_set_checkout_tip, uber_eats_apply_promo

11. Only after explicit confirmation: uber_eats_place_order (API submit first, browser fallback if needed)

Run without MCP (debug)

uv run uber-eats-mcp

or

uv run python server.py

Environment (optional)

Headed browser (login, address, discovery): only one flow runs at a time. If the assistant triggers uber_eats_login twice in parallel, or login runs while another tool opens the same browser, Playwright can error with page or browser has been closed—run one login and wait for it to finish.

What gets stored locally

Session and preferences are under your home directory (e.g. ~/.ubereats-session.json, ~/.ubereats-preferences.json). Do not commit those.

If login fails mid-way, you may see ~/.ubereats-session.json.prelogin.bak (and a matching .ubereats-config.json.prelogin.bak). A successful retry restores them automatically; if *.prelogin.bak exists but the main files do not, you can recover manually: copy each *.prelogin.bak over the non-.bak filename (then remove the .bak files if you like).

Prerequisites

• Python ≥ 3.12

• uv (recommended) or another way to install from pyproject.toml

• Chromium via Playwright (playwright install chromium — included in the setup scripts)

More tools

See the tool list in Cursor/Claude Code after connecting. The implementation lives in server.py.

Legal

This project is unofficial and not affiliated with, endorsed by, or sponsored by Uber Technologies, Inc. in any way. Uber Eats and the Uber logo are trademarks of Uber Technologies, Inc.

• Reverse-engineered APIs. This tool interacts with Uber Eats through undocumented internal APIs captured from browser network traffic. These APIs are not publicly supported and may change or break without notice.

• Terms of Service. Automating interactions with Uber Eats may violate their Terms of Service. You are solely responsible for ensuring your use complies with Uber's terms. The authors take no responsibility for account suspensions, bans, or any other consequences arising from use of this tool.

• No warranty. This software is provided as-is. Orders placed through this tool are your responsibility — always verify your order, delivery address, and payment method before confirming.

• Use responsibly. Do not use this tool for bulk ordering, scraping, or any activity that places undue load on Uber's infrastructure.