TripAdvisor Scraper – Hotels, Restaurants & Reviews

TripAdvisor Scraper is an Apify Actor for travel, lead generation, and MCP workflows that accepts either search queries or direct TripAdvisor URLs. It returns one dataset record per scraped listing or review, so each item represents one hotel, restaurant, attraction, or review entry. Listing records can include name, category, address, city, country, phone, website, rating, review count, price range, ranking, amenities, cuisines, awards, coordinates, and TripAdvisor URLs. Review records can include the linked listing name and URL, review title, text, date of stay, travel type, reviewer details, helpful votes, owner response, published date, and source URL.

Best fit and connected workflows

Use this Actor when you want TripAdvisor data in a structured dataset that is ready for analysis, enrichment, or automation.

It fits well when you want to:

• turn a destination search like "hotels in Miami" into a set of listing URLs and scraped records
• start from a known TripAdvisor Hotel_Review, Restaurant_Review, or Attraction_Review URL and extract listing data
• collect review records for a specific property from provided TripAdvisor listing URLs
• feed structured travel data into CRM, enrichment, monitoring, or agent workflows
• use an Apify Actor through Apify MCP, with a consistent record shape that is easy to interpret

Focused standalone workflow

This Actor is designed as a focused standalone workflow.

One practical scenario

A travel analyst opens Apify with the starting query "restaurants in Paris 1st arrondissement". They run the Actor in listings_and_reviews mode with a small result cap. The returned dataset includes restaurant names, ratings, review counts, price ranges, and TripAdvisor URLs. For restaurants with reviews enabled, the analyst also sees review title, text, travel type, reviewer name, and published date. Based on those fields, the analyst decides which venues to shortlist for deeper review analysis, then exports the dataset to compare reputation and contact details across the target area.

Input fields

Field	Type	Purpose	Default
searchQueries	array of strings	Search terms such as "hotels in Miami" or "restaurants in Paris"	[]
startUrls	array of request objects	Direct TripAdvisor listing URLs, including Hotel_Review, Restaurant_Review, and Attraction_Review pages	[]
scrapeMode	string	listings_and_reviews, listings_only, or reviews_only	listings_and_reviews
maxResults	integer	Maximum number of listing pages to scrape	10
maxReviewsPerListing	integer	Maximum reviews collected per listing when reviews are included	20
proxyConfiguration	object	Apify Proxy configuration	Residential proxy preset

Focused input example

{
  "searchQueries": ["hotels in New York City"],
  "scrapeMode": "listings_and_reviews",
  "maxResults": 5,
  "maxReviewsPerListing": 10,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Output fields

TripAdvisor Scraper writes records to the default dataset. Use record_type to separate listing and review items.

Field	Type	Meaning
record_type	string	listing or review
name	string or null	Name of the hotel, restaurant, or attraction
category	string or null	TripAdvisor top-level category
subcategory	string or null	More specific type within the category
address	string or null	Full street address
city	string or null	City
country	string or null	Country
phone	string or null	Listed phone number
website	string or null	External website URL
rating	number or null	TripAdvisor rating
review_count	integer or null	Total review count
price_range	string or null	$, $$, $$$, or $$$$
price_level	integer or null	Numeric price level
ranking	string or null	Ranking text
ranking_position	integer or null	Numeric ranking position
ranking_total	integer or null	Total in the category and location
description	string or null	TripAdvisor description
amenities	array	Listing amenities
cuisines	array	Restaurant cuisine types
awards	array	TripAdvisor awards
latitude	number or null	Latitude coordinate
longitude	number or null	Longitude coordinate
photos_count	integer or null	Total photos on the listing
tripadvisor_id	string or null	Location ID from the URL d parameter
tripadvisor_url	string	Direct TripAdvisor listing URL
listing_name	string or null	Property name on review records
listing_url	string or null	TripAdvisor URL for the reviewed property
title	string or null	Review title
text	string or null	Review text
date_of_stay	string or null	Date of stay
travel_type	string or null	Travel party type
reviewer_name	string or null	Reviewer username
reviewer_location	string or null	Reviewer home location
helpful_votes	integer or null	Helpful vote count
owner_response	string or null	Management response
published_date	string or null	Review publish date
scraped_at	string	ISO 8601 scrape timestamp
source_url	string	Exact URL used for the scrape

Focused output example

{
  "record_type": "listing",
  "name": "The Setai Miami Beach",
  "category": "Hotel",
  "subcategory": "Luxury Hotel",
  "city": "Miami Beach",
  "country": "United States",
  "rating": 4.5,
  "review_count": 2341,
  "price_range": "$$$$",
  "ranking": "#3 of 233 Hotels in Miami Beach",
  "phone": "+1-305-520-6000",
  "website": "https://www.thesetaihotel.com",
  "tripadvisor_url": "https://www.tripadvisor.com/Hotel_Review-g294471-d234567-Reviews-The_Setai-Miami_Beach.html",
  "scraped_at": "2026-04-09T10:00:00.000Z",
  "source_url": "https://www.tripadvisor.com/Hotel_Review-g294471-d234567-Reviews-The_Setai-Miami_Beach.html"
}

How it works

The Actor accepts either search queries or direct TripAdvisor listing URLs.

• searchQueries is used for destination or topic searches, such as "hotels in Miami" or "restaurants in Paris"
• startUrls is used for direct listing pages, including Hotel_Review, Restaurant_Review, and Attraction_Review URLs
• scrapeMode controls whether the execution collects listings, reviews, or both
• maxResults sets the maximum number of listing pages to process
• maxReviewsPerListing controls how many reviews are collected from each listing when reviews are included
• proxyConfiguration uses Apify Proxy, with a residential preset in the input schema

The browser path uses bounded DataDome retries. Small listings-only probes use a SerpAPI public-index path first so automated tests do not spend five minutes on one blocked residential tunnel; richer jobs keep the browser as primary. The public-index path accepts only genuine TripAdvisor Hotel_Review, Restaurant_Review, and Attraction_Review pages. It returns their indexed listing metadata and rejects browse pages, third-party URLs, and synthetic diagnostic rows.

Evergreen pricing

This Actor uses Pay per event pricing plus standard Apify platform usage. The live Pricing tab shows the current pricing details.

Charged events:

• Listing Scraped: $0.003 per hotel, restaurant, or attraction listing scraped
• Review Scraped: $0.001 per review scraped
• Actor Start: $0.00005 per start event, charged according to the Actor memory allocation, with one event per GB and a minimum of one event

For an example, 10 listings anda bounded number of reviews create 35 pay-per-event records, plus the Actor Start event for the run. For current usage details and any platform-level costs, check the live Pricing tab in Apify.

Use with AI agents (MCP)

This Actor is usable through Apify MCP as a structured tool for retrieving TripAdvisor listings and reviews.

Tool description: retrieve TripAdvisor listing and review records in a predictable schema, with separate handling for destination searches, direct listing URLs, and review collection depth.

Actor identity: khadinakbar/tripadvisor-scraper

Find TripAdvisor hotels in Lisbon, then return the listing records with rating, review count, price range, ranking, phone, website, and TripAdvisor URL. If reviews are included, summarize the review records separately by listing name and published date.

How to interpret the output:

• record_type = listing means the record describes a hotel, restaurant, attraction, or vacation rental
• record_type = review means the record describes one TripAdvisor review tied to a listing
• tripadvisor_url and source_url provide provenance for the scraped record
• listing_name and listing_url connect review records back to the parent listing
• scraped_at shows when the record was collected

Scope, pagination, and cost guidance:

• use searchQueries for topic or destination discovery
• use startUrls for known TripAdvisor pages
• set maxResults to keep discovery runs focused
• set maxReviewsPerListing to control review volume
• review records are charged per item, so review depth directly affects event count
• Apify platform usage still applies alongside Pay per event pricing

Apify API example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/tripadvisor-scraper').call({
  searchQueries: ['restaurants in Rome'],
  scrapeMode: 'listings_and_reviews',
  maxResults: 5,
  maxReviewsPerListing: 10,
});

const datasetItems = await client.dataset(run.defaultDatasetId).listItems();

const listings = datasetItems.items.filter((item) => item.record_type === 'listing');
const reviews = datasetItems.items.filter((item) => item.record_type === 'review');

console.log(`Listings: ${listings.length}`);
console.log(`Reviews: ${reviews.length}`);
console.log(`Dataset ID: ${run.defaultDatasetId}`);

Best results and outcome guidance

• Use search queries when you want TripAdvisor to surface relevant listing URLs for a city or topic.
• Use direct URLs when you already have a specific hotel, restaurant, or attraction page.
• Use listings_only when you want listing metadata without review text.
• Use reviews_only when the source URLs already point to the listings you want to analyze.
• Start with a small maxResults value, then expand once the query shape looks right.
• Set maxReviewsPerListing to match the depth you actually need for analysis or enrichment.
• Filter the dataset by record_type before transforming records into downstream tables or agent prompts.

Design note

I found that the dataset schema uses a single record_type discriminator with two values, listing and review, which makes it straightforward to separate venue data from individual review data in downstream processing.

FAQ

When should I use searchQueries instead of startUrls?

Use searchQueries for destination-based requests like "hotels in Barcelona". Use startUrls when you already have a specific TripAdvisor Hotel_Review, Restaurant_Review, or Attraction_Review page.

What kind of records does this Actor return?

It returns listing records, review records, or both, depending on scrapeMode. Each record is one dataset item.

How do I connect reviews back to a listing?

Review records include listing_name and listing_url, which point back to the parent property.

Can I use this Actor for hotels, restaurants, and attractions?

Yes. The actor title and dataset schema cover those TripAdvisor categories, and the input is built for both search-driven and URL-driven workflows.

Is this Actor usable through Apify MCP?

Yes. It is an Apify Actor with MCP-ready structured output, and the actor identity is khadinakbar/tripadvisor-scraper.

How is pricing calculated?

Pricing follows Pay per event. Listing records, review records, and Actor Start events each have their own event type. Check the live Pricing tab for the current platform and usage details.

Responsible use

Use this Actor for lawful, policy-compliant collection of publicly available TripAdvisor data. Make sure your use of the output fits your internal privacy, access, and data-handling requirements, and review TripAdvisor's terms and any applicable regulations before building production workflows around the results.