Food delivery analysts
Coverage research
Favorable to scraping
Build a starter sheet of restaurant pages for a city, category, or brand query, then compare store availability across locations without copying page titles by hand.
Food Delivery$50Free
Uber Eats Restaurant Listing Scraper
This Uber Eats restaurant listing scraper turns a prepared list of public Uber Eats store URLs into a clean CSV. It is built for teams that need to scrape Uber Eats, export restaurant names, and keep the resulting store links in a structured file without rebuilding a browser automation flow from scratch.
Output
CSV
Columns
3
Input mode
Multi-URL
Source
Public stores
Template
Free import
At a glance
A local Uber Eats data extractor for restaurant research
Exports the fields teams actually reconcile
The template focuses on the practical listing fields in the workflow JSON: keyword, restaurant name, and website. That makes the Uber Eats to CSV output easy to join with territory plans, menu audits, delivery coverage checks, or CRM research.
Runs through store URLs one by one
Uber Eats search pages can require a location before listings are visible. This template uses a multi-URL navigation loop instead, so you can replace the starter URLs with the store pages you discovered for a target keyword or address set.
Keeps the scrape under your control
The browser run and Structured Export happen inside the desktop app. There is no cloud actor queue in the supplied graph, and the CSV stays in the save location you configure.
Avoids per-row marketplace billing
Import the JSON once, adjust the URL list, and rerun when you need a fresh restaurant listing export. It is a practical alternative when a hosted Uber Eats scraper is more infrastructure than the job requires.
Who uses it
Who needs Uber Eats restaurant data in CSV
Restaurant growth teams
Competitive checks
Favorable to scraping
Track which nearby operators appear on Uber Eats, keep website links for follow-up, and hand a consistent CSV to sales or operations teams.
Agencies and researchers
Market mapping
Nuanced outcome
Collect a controlled sample of public store URLs for category research, then enrich it with sibling tools like the Website Contact Details Scraper.
How it works
From Uber Eats store URLs to a structured export
1
Import the JSON template
Download the hosted template from this page and import it into UScraper.
2
Replace the starter store URLs
Edit the Navigate block URLs with the Uber Eats restaurant pages for your keyword, city, or address-based research set.
3
Let the page load and settle
The workflow waits for page load, clicks common consent buttons when present, sleeps briefly, and confirms the page body is visible.
4
Append structured rows
Structured Export writes keyword, restaurant_name, and website to CSV in append mode, then Loop Continue advances to the next URL.
5
Open the CSV
Review the file in Excel, Sheets, or your BI workflow, then rerun with a new URL list when you need another batch.
Output preview
What the Uber Eats restaurant scraper exports
uber-eats-restaurant-listing-scraper.csvColumn
keyword
The configured search label for the URL set, such as dominos or pizza.
Column
restaurant_name
The visible H1, social title fallback, document title fallback, or store path fallback.
Column
website
The final Uber Eats store URL captured from the browser location.
Sample rows
3 of many
| keyword | restaurant_name | website |
|---|---|---|
| dominos | Domino's Pizza Bristol Emersons Green | |
| dominos | McDonald's Fishponds Road | |
| dominos | Greggs Bristol Gloucester Rd North |
Headers included - each URL appends one restaurant row - open in Excel, Sheets, or BI tools
For wider research, pair this workflow with the Google SERP Scraper to discover restaurant pages, the DuckDuckGo Search Results Scraper for another search source, and the Email & Social Finder when exported store websites need contact enrichment.
Frequently asked questions
Automating Uber Eats can implicate Uber terms, robots guidance, restaurant rights, privacy rules, and local laws even when store pages are publicly visible. Use conservative pacing, do not bypass access controls, and get legal review before resale, enrichment at scale, or regulated use.
Before you scale
Practical limits and maintenance notes
Limitations worth checking before larger runs
URL discovery
Search pages may need an address before listings appear
This template expects store URLs in the Navigate block. If you need a different keyword or location, discover the store URLs first, then paste them into the URL array.
Pacing
Keep batches modest and review failures
Heavy unattended runs can trigger throttling, consent loops, or incomplete pages. Start with a small batch, confirm row quality, and increase pacing only after the export is stable.
Selectors
Restaurant names depend on the current page layout
The export tries the visible heading first, then metadata and URL path fallbacks. If Uber Eats changes its page structure, blank names are a signal to update the extraction block.
Browse more workflows in the UScraper template library or install the local desktop app from uscraper.io/download before importing this Uber Eats restaurant scraper.
Get Started
Download and use this template instantly
$50Free
What's Included
- Template JSON file ready to import
- Pre-configured scraping nodes
- Works with UScraper desktop app
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