Uber Eats Menu Scraper 🍔

Turn any Uber Eats restaurant page into a clean, flat menu table — one row per item with the price right at the top level. No nested JSON to dig through, no browser, no manual copy-paste.

What you get (one row per menu item)

field	description
restaurant_name	Store / restaurant name
section	Menu section breadcrumb (e.g. Breakfast > Featured items)
item_name	Menu item name
price_usd	Item price, top-level number (no nested offers.price)
price_currency	Currency code (e.g. USD)
description	Item description
image_url	Item photo
rating / num_ratings	Store rating + review count (repeated on every row)
cuisine	Cuisine tags
price_range	Store price range (e.g. $$)
phone, address, city, state, zip, country	Store location & contact
store_url, source_url	Links
parse_confidence, warnings	Structural-drift signal — you never pay for a broken parse
scraped_at	UTC timestamp

How to use

1. Open a restaurant on ubereats.com and copy the page URL (https://www.ubereats.com/store/{name}/{id}).
2. Paste one or more URLs into Store URLs.
3. Run. You get a flat table of every menu item with its price.

Input

• Store URLs — list of Uber Eats /store/ URLs.
• Max Menus — cap on number of menus (0 = unlimited).
• Delay between requests — politeness delay (default 1.5s).
• Proxy — Apify RESIDENTIAL by default (recommended for reliable access).

Pricing & reliability

• Billed per menu (one charge per restaurant successfully scraped), no matter how many items the menu has — far cheaper than tools that meter the same per-menu unit at a premium.
• You never pay for a failed parse: if the menu structure drifts, rows are returned with a low parse_confidence and the menu is not charged.
• Reliable access via residential proxy; runs entirely on the Apify platform.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Vinted Marketplace
• Allegro Offers
• Amazon Products Scraper
• Autoscout24 Cars Scraper
• Competitor Price Monitor
• Craigslist Scraper

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Use it from your existing tools

Use with Claude Desktop / Cursor / Cline (MCP)

Load this actor as a tool in your AI assistant. Call it directly from your AI assistant via the Apify MCP server — no Store browsing needed. Paste this into your MCP client config (e.g. claude_desktop_config.json) and restart the client:

{
  "mcpServers": {
    "apify-ubereats-menu": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--tools",
        "bovi/ubereats-menu"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_TOKEN"
      }
    }
  }
}

Replace YOUR_APIFY_TOKEN with your own Apify API token (free at apify.com → Settings → Integrations). Curated to a handful of tools so the agent selects reliably.

Works with Clay

Run this actor as an HTTP enrichment step inside a Clay table:

• Method: POST
• URL: https://api.apify.com/v2/acts/bovi~ubereats-menu/run-sync-get-dataset-items?token={{apify_token}}
• Body (JSON): map your Clay columns to the actor input (see the Input section above), e.g. {"storeUrls": "{{clay_column}}"}

The run finishes synchronously and returns the dataset rows straight into your Clay table. It runs on Apify's cloud under your own token and usage. Synchronous runs must complete within 300 seconds.