Uber Eats Scraper

Scrape Uber Eats restaurants and full menus by location or store URL — fast, HTTP-only (no browser), no login, and MCP-ready for AI agents. Search any city or address to get restaurant cards (name, rating, ETA, delivery fee, cuisine, store URL), or paste store URLs to extract complete menus with item prices, descriptions, sections, and images.

What it does

• Search by location — give a free-text address or city (geocoded automatically) or exact lat,lng, and get the live list of nearby restaurants with ratings, review counts, ETAs, delivery fees, and store URLs.
• Full menus — paste Uber Eats store URLs, or turn on enrichMenus, to pull complete menus: sections, item names, prices, descriptions, images, and availability.
• Any market — works for any Uber Eats city/region; switch proxy country and localeCode to match.

When to use it

• Food-delivery market research and competitive analysis — track competitor menus, prices, and coverage city-by-city.
• Pricing intelligence — monitor item prices and delivery fees across restaurants over time.
• Lead lists for restaurant-tech / POS / delivery SaaS — names, ratings, and store URLs by area.
• AI agents — a clean tool call: one location or URL in, structured JSON out.

Not for placing orders, scraping individual customer data, or order tracking — this returns public restaurant and menu listings only.

Output

Search mode returns one record per restaurant. Store-URL mode (and enrichMenus) adds the full menu.

Field	Description
name	Restaurant name
rating	Average star rating (e.g. 4.7)
reviewCount	Number of ratings
priceBucket	Price tier ($, $$, $$$) — enriched only
etaText	Estimated delivery/pickup time (e.g. "12 min")
deliveryFeeText	Delivery fee as shown
cuisines	Cuisine tags — enriched only
phone, address, city	Contact + location — enriched only
storeUrl	Canonical Uber Eats store URL
imageUrl / heroImageUrl	Restaurant image
storeUuid	Stable Uber Eats store ID
menuItemCount	Number of menu items returned
menu	Array of { name, price, priceCents, description, section, imageUrl, isAvailable }
cityName, searchLocation, scrapedAt	Run context (ISO 8601 timestamp)

Example menu item

{
  "name": "Spicy Tuna Roll",
  "description": "Tuna, spicy mayo, cucumber, sesame.",
  "price": 12.95,
  "priceCents": 1295,
  "section": "Signature Rolls",
  "isAvailable": true
}

Pricing — Pay Per Event

Event	Price
Actor start	$0.00005
Restaurant scraped (per card)	$0.004
Menu scraped (per full store menu)	$0.008

A 50-restaurant search costs about $0.20. Enriching 20 of them with full menus adds about $0.16. Pay-per-usage (compute + proxy) is also available for very large jobs.

Input

Field	Type	Notes
location	string	Address or city, e.g. New York, NY (geocoded). Default.
locationLatLng	string	lat,lng — overrides geocoding.
startUrls	array	Uber Eats store URLs for direct menu mode.
searchQuery	string	Keyword search (cuisine/dish/brand), e.g. sushi.
enrichMenus	boolean	Fetch full menu per restaurant (default false).
maxRestaurants	integer	Cap restaurants (default 50).
maxMenusToScrape	integer	Cap enriched menus (default 20).
diningMode	enum	DELIVERY or PICKUP.
proxyConfiguration	object	Defaults to Apify Residential US.

Quick start (location search)

{
  "location": "New York, NY",
  "maxRestaurants": 50
}

Full menus from store URLs

{
  "startUrls": [
    "https://www.ubereats.com/store/your-restaurant/STORE_ID"
  ]
}

Search + enrich top menus

{
  "location": "Chicago, IL",
  "searchQuery": "pizza",
  "enrichMenus": true,
  "maxMenusToScrape": 10
}

Using via API

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~uber-eats-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "location": "Austin, TX", "maxRestaurants": 30 }'

Using with AI agents (MCP)

This actor is exposed through the Apify MCP server as khadinakbar/uber-eats-scraper. Point your MCP client at https://mcp.apify.com and call it with a location or startUrls. It returns flat, agent-friendly JSON.

How it works

The actor talks directly to Uber Eats' own JSON endpoints (getFeedV1 for the location feed, getStoreV1 for menus) over plain HTTP — no headless browser, so runs are fast and cheap. Free-text addresses are geocoded with OpenStreetMap Nominatim. Apify Residential proxies are used by default because Uber Eats blocks most datacenter IPs.

FAQ

Do I need cookies or a login? No. The actor is cookieless.

Which countries work? Any Uber Eats market. Set proxyConfiguration country and localeCode to match (e.g. en-GB).

Why residential proxies? Uber Eats blocks datacenter IPs aggressively. Residential is the reliable default.

What if a location has no restaurants? The run finishes cleanly with a warning (no charge for empty results beyond start).

Can I get item-level modifiers/options? Item names, prices, descriptions, sections, and availability are returned. Deep modifier trees may vary by store.

Legal

This actor collects only publicly available data from Uber Eats listing and store pages. Use it in compliance with Uber Eats' Terms of Service and all applicable laws (including data-protection regulations). You are responsible for how you use the scraped data. This tool is not affiliated with, endorsed by, or sponsored by Uber Technologies, Inc. "Uber Eats" is a trademark of its respective owner.