Booking.com Attractions Search Scraper

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Pull tours and activities for a destination you already identified in Booking’s attractions flow: one row per product, with names, pricing hints, review stats, photos, and flags—ready for merchandising, research, or partner reporting.

Who it’s for

• Destination & product teams building activity assortments for a city or region.
• Marketing & partnerships comparing what Booking promotes for a location.
• Analysts who need repeatable exports instead of browsing page by page.

What you can do with it

• Collect many products across result pages until you hit your cap.
• Optional filters and sort when you already know valid values from the same Booking workflow (currency, language, type/price/UFI/label filters).
• Date range fields when you use them in your search context.

How it works (in plain terms)

You provide the location / product id (usually from searchLocation—often a Base64-style string). The tool requests page 1, 2, … of the attractions catalog, merges each product into your dataset, and stops when there are no more products or you reach your maximum row count.

Input

Output

• One row per attraction product until your cap or the end of results.
• Identity & copy: product id, name, URL slug, and shortDescription. Nested objects often include GraphQL-style __typename labels from the upstream API.
• Pricing: representativePrice with charge/public amounts and currency (e.g. INR).
• Media: primaryPhoto, usually with a small image URL (long-lived CDN links; treat as snapshot).
• Reviews: reviewsStats—including allReviewsCount, percentage, and combinedNumericStats (average, total review count used for scoring).
• Place: ufiDetails—city name (bCityName), ufi, and url.country.
• Policies & commerce: cancellationPolicy (e.g. hasFreeCancellation), offers (nested offer items with their own ids), supportedFeatures (e.g. nativeApp).
• Merchandising: flags (e.g. bestseller, recommended-by-travellers, guide badges) with flag, value, and rank.
• Every row adds when that record was written (scrapedAt).

Illustrative shape only: some products may omit or add fields; image URLs are long.

Sample output (one dataset row, trimmed)

{
"__typename":"AttractionsProduct",
"cancellationPolicy":{
"__typename":"AttractionsCancellationPolicy",
"hasFreeCancellation":true
},
"id":"PR6K7ZswbGBs",
"name":"Dharavi, Dhobighat, and Mumbai Dabbawallas",
"slug":"pr6k7zswbgbs-mumbai-half-day-tour",
"shortDescription":"A must do tour of Mumbai. This is an off-beat 3.5hours tour where you will see Mumbai from a wide...",
"representativePrice":{
"__typename":"AttractionsPrice",
"chargeAmount":1852.75,
"currency":"INR",
"publicAmount":1852.75
},
"primaryPhoto":{
"__typename":"AttractionsPhoto",
"small":"https://r-xx.bstatic.com/xdata/images/xphoto/300x320/147437415.jpg?k=3269ed4a34e76c66d327cfb870fa07ce95629ab36021dc0b11924f0379ccb046&o="
},
"reviewsStats":{
"__typename":"AttractionsProductReviewStats",
"allReviewsCount":0,
"percentage":"0",
"combinedNumericStats":{
"__typename":"AttractionsProductCombinedReviewStats",
"average":4.7,
"total":87
}
},
"ufiDetails":{
"__typename":"AttractionLocationResponse",
"bCityName":"Mumbai",
"ufi":-2092174,
"url":{
"__typename":"AttractionLocationUrl",
"country":"in"
}
},
"offers":[
{
"__typename":"Offer",
"items":[{"__typename":"OfferItem","id":"OIEnoHP8TJsB"}]
},
{
"__typename":"Offer",
"items":[{"__typename":"OfferItem","id":"OIMyfydyr7zh"}]
}
],
"supportedFeatures":{
"__typename":"AttractionsProductSupportedFeatures",
"nativeApp":true
},
"flags":[
{
"__typename":"AttractionsProductFlags",
"flag":"recommendedByTravellers",
"value":true,
"rank":96
},
{
"__typename":"AttractionsProductFlags",
"flag":"bestseller",
"value":true,
"rank":1
},
{
"__typename":"AttractionsProductFlags",
"flag":"aiBadgesExpertGuide",
"value":true,
"rank":1
}
],
"scrapedAt":"2026-03-25T06:11:09.965Z"
}

Good to know

• The id must match what searchLocation (or your upstream step) returns; wrong values yield empty or failed searches.
• Filter tag names come from a first response’s filterOptions when you need narrow segments.
• Use data in line with Booking.com’s terms and applicable laws.