# Uber Eats Menu Scraper · Items, Prices & Categories (`corent1robert/uber-eats-menu-scraper`) Actor Export every Uber Eats menu item with section, price, and description — one row per dish for pricing benchmarks. Search by city, territory, or paste store URLs. No login or API key - **URL**: https://apify.com/corent1robert/uber-eats-menu-scraper.md - **Developed by:** [Corentin Robert](https://apify.com/corent1robert) (community) - **Categories:** Automation, E-commerce, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 menu item extracteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Uber Eats Menu Scraper **Built for pricing teams, F&B brands, dark kitchens, and market researchers** — export every Uber Eats dish with section, price, and description in one flat spreadsheet. **No login. No API key. No Uber Eats account.** Item names, prices, menu sections, and store context ship on every row. Search by **city**, **territory**, or paste **store URLs** — same discovery as the Pro Scraper, but each dataset row is a **menu item**, not a CRM lead. --- ### Who is this for? | You are… | Typical goal | Suggested setup | |----------|--------------|-----------------| | **Pricing / revenue ops** | Benchmark competitor menu prices in a city | **City** or **URLs** | | **F&B brand / supplier** | Map categories and price bands across a market | **Territory** + cuisine filter | | **Dark kitchen / franchise** | Compare menus across locations of the same brand | **URLs** — bulk paste store links | | **Consultant / agency** | Deliverable: full menu export for a client market | **City** + raise `maxResults` | | **Market research** | Price distribution and category mix by geo | **Territory** with `maxResults` cap | | **Data / ops** | Scheduled refresh on a known restaurant list | **URLs** mode | **By default** you get `itemName`, `section`, `price`, `currency`, `storeName`, and `city` — **one row per dish** (`outputMode: flat`). Descriptions and store ratings are included when Uber Eats publishes them on the store page. **Use `deduplicateItems: true`** (default) when the same burger appears in “Featured” and “Burgers”. Turn it **off** only if you need every section occurrence for audit trails. Use **`outputMode: nested`** when you want one JSON object per restaurant with an `items[]` array (API workflows). --- ### What you get out of it | Benefit | Why it matters | |--------|----------------| | **Spreadsheet-ready pricing** | Flat export — filter by `sectionTop`, pivot on `itemName`, compare `price` across `storeName` | | **Same discovery as Pro** | City, territory, and URL modes — no second tool to list restaurants | | **Honest menu depth** | Full item list from public store pages — not just min/max price bands | | **Geo context on every row** | `city`, `country`, `storeId` — join runs or dedupe across exports | | **Transparent runs** | Logs show **progress %**, **ETA**, and a downloadable **RUN_LOG** | --- ### How much does it cost to scrape Uber Eats menus? Pricing is **pay-per-event** (plus light HTTP compute — **no proxy required** for menus): | Event | Price | When charged | |-------|-------|----------------| | `apify-actor-start` | **$0** | Every run (no run fee) | | `uber-eats-menu-item` | **$0.003** | Each menu row (**flat** mode) | | `uber-eats-store-with-menu` | **$0.03** | One row per store (**nested** mode) | | `uber-eats-store-no-menu` | **$0.005** | Store page returned no usable menu | | `apify-default-dataset-item` | **$0** | Guard — avoids double-charging | | Scenario | Listed PPE (items only) | Typical total | |----------|-------------------------|---------------| | Try: 3 stores, ~300 items | **~$0.90** | **~$0.95–1.05** with compute | | 15 stores, ~1 400 items (City Annecy) | **~$4.20** | **~$4.50–5.50** | | 1 000 menu items | **~$3.00** | **~$3.50–4.50** | | 10 000 menu items | **~$30.00** | **~$32–40** | HTTP-only runs keep compute low. There is **no residential proxy** on this Actor — menus are read from public store HTML. **Console setup (Monetization):** set `apify-actor-start` and `apify-default-dataset-item` to **$0**; add `uber-eats-menu-item` (**$0.003**), `uber-eats-store-with-menu` (**$0.03**), `uber-eats-store-no-menu` (**$0.005**). --- ### Is scraping Uber Eats free? Apify charges for Actor runs (PPE + compute). A **3-store Try** (~300 menu rows) is usually about **$1** all-in (items + light compute). There is no separate Uber Eats API fee — this Actor uses public store pages only. --- ### Is it legal to scrape Uber Eats? This Actor only reads **public restaurant and menu pages** that Uber Eats shows to consumers. You are responsible for using the data in line with GDPR, local marketing laws, and Uber Eats' terms. Do not use scraped data for unlawful competitive practices or misrepresentation. --- ### Quick start 1. Open **Input** → leave **Search mode** on **City** (default) 2. Enter a city name (e.g. `Annecy`) — country is inferred automatically 3. **Max restaurants** defaults to **15**; clear **Cuisine filter** to include all restaurants in the city 4. Leave **Include descriptions** on; keep **Deduplicate items** on for cleaner spreadsheets 5. Click **Run** → open **Dataset** → export CSV, JSON, or Excel For a **single competitor**, switch to **URLs** mode and paste one store link. --- ### Search modes #### City — One or more cities (default) Best for **local price benchmarks**. Items from each restaurant appear as that store is scraped. ```json { "mode": "city", "cityNames": ["Annecy", "Paris"], "maxResults": 15, "categories": [], "includeDescriptions": true, "deduplicateItems": true, "outputMode": "flat" } ```` Prefix a country to disambiguate: `fr:Annecy`, `us:New York`. #### Territory — Country or region Best for **large market slices**. Set a **Max restaurants** cap unless you need a full-country sweep. ```json { "mode": "territory", "country": "fr", "region": "fr:ara", "categories": ["burger"], "maxResults": 500, "outputMode": "flat" } ``` Set **`maxResults`: `0`** for no restaurant ceiling (use a long **Run timeout** in Run options). #### URLs — Specific restaurants Best for **head-to-head competitor menus**. ```json { "mode": "urls", "startUrls": [ { "url": "https://www.ubereats.com/fr/store/mcdonalds-epagny/b4043WTlR_iSrNu0-Y2DYQ" }, { "url": "https://www.ubereats.com/fr/store/burger-king-epagny/ZOjYdsqgQcqPQ5q40GJoXQ" } ], "includeDescriptions": true, "outputMode": "flat" } ``` *** ### Input parameters | Field | Default | Description | |-------|---------|-------------| | `mode` | `city` | `city`, `territory`, or `urls` | | `cityNames` | `["Annecy"]` | Cities for City mode | | `country` / `region` | — | Territory mode | | `startUrls` | — | Store URLs for URL mode | | `maxResults` | `15` | Max **restaurants** per city or territory (`0` = no cap) | | `categories` | `["fast-food"]` | Cuisine filter; **empty array** = all cuisines | | `includeDescriptions` | `true` | Item description text | | `deduplicateItems` | `true` | Merge duplicate items across sections | | `outputMode` | `flat` | `flat` = one row per item; `nested` = one row per store | | `sectionFilter` | — | API-only: regex or string list to keep matching sections | *** ### Output fields (flat mode) | Group | Fields | |-------|--------| | Store | `storeName`, `storeUrl`, `storeId`, `storeSlug`, `city`, `country` | | Menu | `section`, `sectionTop`, `itemName`, `description`, `price`, `currency` | | Context | `rating`, `reviewCount`, `itemIndex`, `scrapedAt` | | Errors | `error` (only on failed stores in edge cases) | CSV uses **semicolon** separators when exported locally for Excel-friendly import. *** ### Typical coverage (reference runs) Based on HTTP smoke runs on French store pages: | Field | Coverage | |-------|----------| | `itemName` | ~100% of extracted items | | `price` / `currency` | ~100% | | `description` | ~95% (chains sometimes omit on promos) | | `section` / `sectionTop` | ~100% | | Item images | Not exported in v1 (planned v1.1) | | Modifiers / sizes / add-ons | Not in public JSON-LD — not exported | `maxResults` limits **restaurants**, not menu rows. One McDonald's location can yield **150–200+** items. *** ### Pricing & research workflows | Goal | Filter / columns | Suggested input | |------|------------------|-----------------| | **City price map** | Group by `storeName`, compare `price` on `itemName` | City mode, `categories: []` | | **Category benchmark** | Filter `sectionTop` = `Burgers` or `Menus` | City or URLs | | **Chain vs indie** | Compare `storeName` across URLs | URLs mode, 5–10 stores | | **Deduped master menu** | `deduplicateItems: true` | Default | | **Refresh cadence** | Key on `storeId` + `itemName` + `price` | URLs list from Pro Scraper export | **Spreadsheet tip:** pivot on `sectionTop` and `storeName`; use `price` as values. Filter `currency` when mixing US and EU stores. *** ### How it works 1. **Discover** restaurants (city / territory listing, or your URLs) 2. **Fetch** each public store page (HTTP, browser-like headers) 3. **Parse** menu from structured page data (`hasMenu` → sections → items) 4. **Normalize** rows — optional dedupe and section filter 5. **Export** to Dataset (and local CSV on `apify run`) *** ### Output example One menu item from a **City · Annecy · 3 restaurants** run: ```json { "storeUrl": "https://www.ubereats.com/fr/store/mcdonalds-epagny/b4043WTlR_iSrNu0-Y2DYQ", "storeId": "b4043WTlR_iSrNu0-Y2DYQ", "storeSlug": "mcdonalds-epagny", "storeName": "McDonald's® (Epagny)", "city": "EPAGNY", "country": "FR", "section": "NOUVEAUTÉS", "sectionTop": "NOUVEAUTÉS", "itemName": "BEEF MOZZA CHEDDAR", "description": "Pain spécial, fromage fondu, steak haché, préparation panée à base de mozzarella et de cheddar, tomate, salade, oignons, sauce.", "price": 14.7, "currency": "EUR", "rating": 3.9, "reviewCount": 4000, "itemIndex": 12, "scrapedAt": "2026-06-08T08:00:00.000Z" } ``` *** ### Limitations - **Modifiers, sizes, and add-ons** are not exposed in public menu JSON — not exported in v1. - **Item images** are only partially present in page HTML — full image pass planned for v1.1. - Large **territory** runs without `maxResults` can take hours and produce **hundreds of thousands** of menu rows — set caps and timeout accordingly. - Uber Eats may show **promotional sections** (e.g. “buy one get one”) — items can appear in multiple sections unless dedupe is on. *** ### Also available - **[Uber Eats Pro Scraper](https://apify.com/corent1robert/uber-eats-pro-scraper)** — CRM-ready restaurant leads: phones, emails, company IDs, SIRET, territory search. Use Pro to **list restaurants**, then paste `url` values into this Actor's **URLs** mode for full menus. - **[Uber Eats Reviews Scraper](https://apify.com/corent1robert/uber-eats-reviews-scraper)** — monitor store ratings and latest public customer comments for the same store URLs (reputation snapshot, not full review history). *** ### Local development ```bash npm install npm run build:schema npm run build npm test mkdir -p storage/key_value_stores/default cp input.json storage/key_value_stores/default/INPUT.json apify run ``` `apify run` validates `storage/key_value_stores/default/INPUT.json` against the input schema — include every field you need, or use `apify run --input-file=./input.json`. Local runs write **`output.csv`** (UTF-8 BOM, semicolon-separated) next to the project root. *** ### Support Contact for custom Uber Eats pipelines, menu monitoring, or hub Actor integrations. # Actor input Schema ## `mode` (type: `string`): Same discovery as the Pro Scraper — output is **menu items**, not contact leads. ## `cityNames` (type: `array`): One city per line — e.g. `Annecy`, `Paris`. Prefix `fr:Annecy` to force France. ## `country` (type: `string`): Target country for the territory sweep. ## `region` (type: `string`): **All regions** for full country, or one region. ## `startUrls` (type: `array`): One Uber Eats store URL per line. ## `maxResults` (type: `integer`): **City:** per city. **Territory:** total. **URLs:** ignored. ## `categories` (type: `array`): Narrow listing. City mode uses the first selected cuisine. ## `includeDescriptions` (type: `boolean`): Item descriptions from Uber Eats (recommended for competitive analysis). ## `deduplicateItems` (type: `boolean`): Merge same item appearing in multiple sections (e.g. featured + category). ## `outputMode` (type: `string`): **Flat** = one dataset row per menu item (spreadsheet). **Nested** = one row per store with items array. ## Actor input object example ```json { "mode": "city", "cityNames": [ "Annecy" ], "country": "fr", "maxResults": 15, "categories": [ "fast-food" ], "includeDescriptions": true, "deduplicateItems": true, "outputMode": "flat" } ``` # Actor output Schema ## `results` (type: `string`): Dataset of menu rows (flat) or stores with nested items. ## `overview` (type: `string`): Table view with restaurant, section, item, and price. ## `runLog` (type: `string`): Text log written during execution. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "cityNames": [ "Annecy" ], "maxResults": 15, "categories": [ "fast-food" ] }; // Run the Actor and wait for it to finish const run = await client.actor("corent1robert/uber-eats-menu-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "cityNames": ["Annecy"], "maxResults": 15, "categories": ["fast-food"], } # Run the Actor and wait for it to finish run = client.actor("corent1robert/uber-eats-menu-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "cityNames": [ "Annecy" ], "maxResults": 15, "categories": [ "fast-food" ] }' | apify call corent1robert/uber-eats-menu-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=corent1robert/uber-eats-menu-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Uber Eats Menu Scraper · Items, Prices & Categories", "description": "Export every Uber Eats menu item with section, price, and description — one row per dish for pricing benchmarks. Search by city, territory, or paste store URLs. No login or API key", "version": "1.2", "x-build-id": "JdadASkxtnbObF27e" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/corent1robert~uber-eats-menu-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-corent1robert-uber-eats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/corent1robert~uber-eats-menu-scraper/runs": { "post": { "operationId": "runs-sync-corent1robert-uber-eats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/corent1robert~uber-eats-menu-scraper/run-sync": { "post": { "operationId": "run-sync-corent1robert-uber-eats-menu-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Search mode", "enum": [ "city", "territory", "urls" ], "type": "string", "description": "Same discovery as the Pro Scraper — output is **menu items**, not contact leads.", "default": "city" }, "cityNames": { "title": "City name(s)", "type": "array", "description": "One city per line — e.g. `Annecy`, `Paris`. Prefix `fr:Annecy` to force France.", "items": { "type": "string" } }, "country": { "title": "Country", "enum": [ "za", "de", "ar", "au", "be", "ca", "cl", "cr", "ec", "es", "us", "fr", "gt", "ie", "it", "jp", "ke", "lu", "mx", "nz", "pa", "nl", "pl", "pt", "do", "gb", "sv", "lk", "se", "ch", "tw" ], "type": "string", "description": "Target country for the territory sweep.", "default": "fr" }, "region": { "title": "Region", "enum": [ "za:all", "za:ec", "za:fs", "za:gp", "za:kzn", "za:lp", "za:mp", "za:nw", "za:nc", "za:wc", "de:all", "de:bw", "de:by", "de:be", "de:bb", "de:hb", "de:hh", "de:he", "de:ni", "de:nw", "de:rp", "de:sl", "de:sn", "de:st", "de:sh", "de:th", "de:mv", "ar:all", "au:all", "au:act", "au:nsw", "au:nt", "au:qld", "au:sa", "au:tas", "au:vic", "au:wa", "be:all", "be:anvers", "be:brabant-flamand", "be:brabant-wallon", "be:bruxelles-capitale", "be:flandre-occidentale", "be:flandre-orientale", "be:hainaut", "be:limbourg", "be:liège", "be:namur", "be:luxembourg", "ca:all", "ca:ab", "ca:bc", "ca:mb", "ca:nb", "ca:nl", "ca:nt", "ca:ns", "ca:on", "ca:pe", "ca:qc", "ca:sk", "ca:yt", "cl:all", "cl:ai", "cl:an", "cl:ar", "cl:ap", "cl:at", "cl:bi", "cl:co", "cl:li", "cl:ll", "cl:lr", "cl:ma", "cl:ml", "cl:rm", "cl:ta", "cl:vs", "cl:nb", "cr:all", "cr:alajuela", "cr:cartago", "cr:guanacaste", "cr:heredia", "cr:limón", "cr:puntarenas", "cr:san-josé", "ec:all", "ec:azuay", "ec:chimborazo", "ec:cotopaxi", "ec:el-oro", "ec:guayas", "ec:imbabura", "ec:manabí", "ec:pichincha", "ec:santa-elena", "ec:santo-domingo-de-los-tsáchilas", "ec:tungurahua", "ec:zamora-chinchipe", "es:all", "es:es-an", "es:es-ar", "es:es-cn", "es:es-cb", "es:es-cl", "es:es-cm", "es:es-ct", "es:es-ce", "es:es-nc", "es:es-md", "es:es-vc", "es:es-ex", "es:es-ga", "es:es-ib", "es:es-ri", "es:es-ml", "es:es-pv", "es:es-as", "es:es-mc", "us:all", "us:aguada", "us:aguadilla", "us:aibonito", "us:al", "us:ak", "us:arecibo", "us:az", "us:ar", "us:añasco", "us:barceloneta", "us:barranquitas", "us:bayamón", "us:cabo-rojo", "us:caguas", "us:ca", "us:canóvanas", "us:carolina", "us:cataño", "us:cayey", "us:ceiba", "us:cidra", "us:co", "us:ct", "us:corozal", "us:de", "us:dc", "us:dorado", "us:fajardo", "us:fl", "us:ga", "us:guayama", "us:guaynabo", "us:gurabo", "us:guánica", "us:hatillo", "us:hi", "us:hormigueros", "us:humacao", "us:id", "us:il", "us:in", "us:ia", "us:isabela", "us:juana-díaz", "us:juncos", "us:ks", "us:ky", "us:lajas", "us:lares", "us:las-piedras", "us:la", "us:loíza", "us:me", "us:manatí", "us:md", "us:ma", "us:maunabo", "us:mayagüez", "us:mi", "us:mn", "us:ms", "us:mo", "us:mt", "us:naguabo", "us:ne", "us:nv", "us:nh", "us:nj", "us:nm", "us:ny", "us:nc", "us:nd", "us:oh", "us:ok", "us:or", "us:pa", "us:peñuelas", "us:ponce", "us:quebradillas", "us:ri", "us:rincón", "us:río-grande", "us:salinas", "us:san-germán", "us:san-juan", "us:san-lorenzo", "us:san-sebastián", "us:santa-isabel", "us:sc", "us:sd", "us:tn", "us:tx", "us:toa-alta", "us:toa-baja", "us:trujillo-alto", "us:ut", "us:vega-alta", "us:vega-baja", "us:vt", "us:vieques", "us:va", "us:wa", "us:wv", "us:wi", "us:wy", "us:yabucoa", "us:yauco", "us:aguas-buenas", "us:luquillo", "fr:all", "fr:ara", "fr:bfc", "fr:bre", "fr:cvl", "fr:cor", "fr:ges", "fr:hdf", "fr:nor", "fr:naq", "fr:occ", "fr:pdl", "fr:pac", "fr:idf", "gt:all", "gt:av", "gt:cm", "gt:es", "gt:gu", "gt:qz", "gt:sa", "ie:all", "ie:cw", "ie:cn", "ie:ce", "ie:co", "ie:dl", "ie:d", "ie:g", "ie:ky", "ie:ke", "ie:kk", "ie:ls", "ie:lm", "ie:lk", "ie:ld", "ie:lh", "ie:mo", "ie:mh", "ie:oy", "ie:rn", "ie:so", "ie:ta", "ie:wd", "ie:wh", "ie:wx", "ie:ww", "it:all", "it:campania", "it:emilia-romagna", "it:lazio", "it:lombardia", "it:piemonte", "it:puglia", "it:toscana", "it:veneto", "jp:all", "jp:mie", "jp:kyoto", "jp:saga", "jp:hyogo", "jp:hokkaido", "jp:chiba", "jp:wakayama", "jp:saitama", "jp:oita", "jp:osaka", "jp:nara", "jp:miyagi", "jp:miyazaki", "jp:toyama", "jp:yamaguchi", "jp:yamagata", "jp:yamanashi", "jp:gifu", "jp:okayama", "jp:iwate", "jp:shimane", "jp:hiroshima", "jp:tokushima", "jp:ehime", "jp:aichi", "jp:niigata", "jp:tokyo", "jp:tochigi", "jp:okinawa", "jp:shiga", "jp:kumamoto", "jp:ishikawa", "jp:kanagawa", "jp:fukui", "jp:fukuoka", "jp:fukushima", "jp:akita", "jp:gunma", "jp:ibaraki", "jp:nagasaki", "jp:nagano", "jp:aomori", "jp:shizuoka", "jp:kagawa", "jp:kochi", "jp:tottori", "jp:kagoshima", "ke:all", "ke:central", "ke:coast", "ke:eastern", "ke:nairobi", "ke:nyanza", "ke:rift-valley", "lu:all", "lu:ca", "lu:es", "lu:gr", "lu:lux", "lu:me", "lu:rm", "mx:all", "mx:ag", "mx:bc", "mx:bs", "mx:cm", "mx:cs", "mx:ch", "mx:co", "mx:cl", "mx:df", "mx:dg", "mx:gt", "mx:gr", "mx:hg", "mx:ja", "mx:mi", "mx:mo", "mx:em", "mx:na", "mx:nl", "mx:oa", "mx:pu", "mx:qt", "mx:qr", "mx:sl", "mx:si", "mx:so", "mx:tb", "mx:tm", "mx:tl", "mx:ve", "mx:yu", "mx:za", "nz:all", "nz:auk", "nz:bop", "nz:can", "nz:gis", "nz:hkb", "nz:mwt", "nz:mbh", "nz:nsn", "nz:ntl", "nz:ota", "nz:tki", "nz:tas", "nz:wko", "nz:wgn", "nz:wtc", "nz:stl", "pa:all", "pa:chiriquí", "pa:panamá", "pa:panamá-oeste", "pa:veraguas", "nl:all", "nl:drenthe", "nl:flevoland", "nl:friesland", "nl:gelderland", "nl:groningen", "nl:limburg", "nl:noord-brabant", "nl:noord-holland", "nl:overijssel", "nl:utrecht", "nl:zeeland", "nl:zuid-holland", "pl:all", "pl:dolnoslaskie", "pl:kujawsko-pomorskie", "pl:lubelskie", "pl:lubuskie", "pl:mazowieckie", "pl:malopolskie", "pl:opolskie", "pl:podkarpackie", "pl:podlaskie", "pl:pomorskie", "pl:warminsko-mazurskie", "pl:wielkopolskie", "pl:zachodniopomorskie", "pl:lódzkie", "pl:slaskie", "pl:swietokrzyskie", "pt:all", "pt:alentejo", "pt:algarve", "pt:centro", "pt:lisboa", "pt:norte", "pt:região-autónoma-da-madeira", "pt:região-autónoma-dos-açores", "do:all", "do:cibao-norte", "do:enriquillo", "do:higuamo", "do:ozama-o-metropolitana", "do:valdesia", "do:yuma", "do:cibao-nordeste", "do:cibao-sur", "gb:all", "gb:eng", "gb:nir", "gb:sct", "gb:wls", "sv:all", "sv:ca", "sv:li", "sv:un", "sv:sm", "sv:ss", "sv:sa", "lk:all", "lk:central", "lk:north-central", "lk:north-western", "lk:northern", "lk:sabaragamuwa", "lk:southern", "lk:uva", "lk:western", "se:all", "se:blekinge", "se:dalarna", "se:gotland", "se:gävleborg", "se:halland", "se:jämtland", "se:jönköping", "se:kalmar", "se:kronoberg", "se:norrbotten", "se:skåne", "se:stockholm", "se:södermanland", "se:uppsala", "se:värmland", "se:västerbotten", "se:västernorrland", "se:västmanland", "se:västra-götaland", "se:örebro", "se:östergötland", "ch:all", "ch:ag", "ch:ar", "ch:bl", "ch:bs", "ch:be", "ch:fr", "ch:ge", "ch:gl", "ch:gr", "ch:ju", "ch:lu", "ch:ne", "ch:nw", "ch:ow", "ch:sh", "ch:sz", "ch:so", "ch:sg", "ch:tg", "ch:ti", "ch:ur", "ch:vs", "ch:vd", "ch:zg", "ch:zh", "tw:all", "tw:nan", "tw:txg", "tw:tpe", "tw:tnn", "tw:ttt", "tw:cyi", "tw:cyq", "tw:kee", "tw:ila", "tw:pif", "tw:cha", "tw:nwt", "tw:hsz", "tw:hsq", "tw:tao", "tw:pen", "tw:hua", "tw:mia", "tw:kin", "tw:yun", "tw:khh" ], "type": "string", "description": "**All regions** for full country, or one region." }, "startUrls": { "title": "Restaurant URLs", "type": "array", "description": "One Uber Eats store URL per line.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxResults": { "title": "Max restaurants", "minimum": 0, "type": "integer", "description": "**City:** per city. **Territory:** total. **URLs:** ignored.", "default": 15 }, "categories": { "title": "Cuisine filter", "type": "array", "description": "Narrow listing. City mode uses the first selected cuisine.", "items": { "type": "string", "enum": [ "halal", "fast-food", "japanese", "pizza", "burger", "sushi", "thai", "chinese", "indian", "mediterranean", "vegetarian", "vegan", "bakery", "breakfast", "kebab", "mexican", "italian", "french", "korean", "american", "seafood", "desserts" ], "enumTitles": [ "Halal", "Fast food", "Japanese", "Pizza", "Burger", "Sushi", "Thai", "Chinese", "Indian", "Mediterranean", "Vegetarian", "Vegan", "Bakery", "Breakfast", "Kebab", "Mexican", "Italian", "French", "Korean", "American", "Seafood", "Desserts" ] }, "default": [ "fast-food" ] }, "includeDescriptions": { "title": "Include descriptions", "type": "boolean", "description": "Item descriptions from Uber Eats (recommended for competitive analysis).", "default": true }, "deduplicateItems": { "title": "Deduplicate items", "type": "boolean", "description": "Merge same item appearing in multiple sections (e.g. featured + category).", "default": true }, "outputMode": { "title": "Output shape", "enum": [ "flat", "nested" ], "type": "string", "description": "**Flat** = one dataset row per menu item (spreadsheet). **Nested** = one row per store with items array.", "default": "flat" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```