Tripadvisor Real-Time Data

Turn Tripadvisor into a clean, structured lead and market-intelligence feed. Collect restaurants, hotels and attractions across multiple markets in a single run — with phone numbers, addresses, coordinates, ratings, review counts, cuisines, descriptions, contact emails and websites — all delivered as clean JSON to your Apify dataset as the run progresses.

Built for lead-generation agencies, hospitality and travel teams, market researchers, and AI/LLM workflows that need reliable Tripadvisor data without maintaining a brittle scraper.

Free plan / paid plan: this Actor is intended for paid Apify plans. Runs on a free Apify account are restricted to a small sample of 2 results and log a clear message inviting you to upgrade. Upgrade to a paid plan for full, unlimited data. See Free plan & paid plans.

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Why this Actor

	Tripadvisor Real-Time Data	Typical DIY scraper
Setup	Organized input UI, run immediately	Fragile scripts to maintain
Memory	512 MB default — results written as they arrive	2–4 GB+, everything held until the end
Speed	Fast structured output	Often slow and unreliable
Output	Consistent, LLM-ready JSON	Inconsistent formats
Scale	Large runs, many markets per run	Usually one query at a time
Lead data	Phones, emails, websites, ratings	Rarely normalized
Delivery	Dataset + optional real-time webhook	Manual export
Reliability	Runs reliably out of the box	Needs constant babysitting

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What you get

Every record is tagged with featureType (listing, place, or review) and scrapedAt, so you can filter, join and pipeline the data anywhere.

Business records — featureType: "listing" or "place"

Field	Description
name	Business name
category	restaurant, hotel or attraction
url	Tripadvisor listing link for the business
telephone	Phone number when published
email, emails[]	Contact email(s) when the business publishes one
website, websites[]	External website(s)
address, city, region, postalCode, country	Full location breakdown
latitude, longitude	Coordinates for mapping and geo filters
rating, reviewCount	Reputation signals
priceRange	Price tier when shown
cuisine[]	Cuisine tags (restaurants)
description	Business summary
geoId, placeId	Tripadvisor identifiers
marketLabel	The market row that produced this record
sourceUrl	The business's Tripadvisor link
scrapedAt	Record timestamp

Review records — featureType: "review"

Field	Description
businessName, businessUrl	The reviewed business
author, authorLocation, authorContributions	Reviewer identity
rating	Star rating (1–5)
title, text	Review headline and body
date	Review date
tripType	Trip type when shown
helpfulVotes	Helpful votes
scrapedAt	Record timestamp

New records are written to the dataset as they arrive — long runs stay light in memory and you can start consuming results before the run finishes.

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Features

🏪 Listing Discovery — on by default

Discover restaurants, hotels or attractions for any number of markets in one run. Each market row is a category + location. Location accepts:

• a common city name (e.g. New York, London, Paris, Las Vegas),
• a numeric Tripadvisor geo ID (e.g. 60763) for any market worldwide, or
• a full Tripadvisor market link.

🎯 Lead details

The Lead details switch collects the full profile for every business found — address details, coordinates, rating, description and contact details (phone, website, email where the business publishes one). Every business is always included in the results; a field simply stays empty when a business does not have that detail, so your dataset is always complete and predictable. Turn it off for a faster, lighter run with business names and links only.

📍 Place Details

Already have a list of Tripadvisor business links? Paste them in and each one is returned as a complete structured profile.

⭐ Reviews

Collect reviews for specific business links — author, rating, date, trip type, text and helpful votes. Each review is its own dataset row linked to the business, ideal for sentiment analysis and competitor monitoring.

🔔 Real-time webhooks

Every record is always saved to the dataset. Optionally add a webhook URL and each new record is also POSTed in real time to your CRM, Slack, Zapier, Make, n8n or Google Sheets. Choose json (full record) or slack (ready-to-post Slack message).

🌐 Connection

Runs use the built-in Apify residential proxy (US) by default, configurable in the Connection section if you need a different country or your own proxy URLs.

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Use cases

• Restaurant lead generation — every restaurant in a city with phone, email, website, rating and cuisine.
• Hospitality prospecting — hotels by market for suppliers, SaaS and service providers.
• Attraction & tourism research — map attractions, their ratings and review volume per destination.
• Multi-market expansion — one run across dozens of city + category combinations.
• Competitor intelligence — compare ratings, review counts and price ranges across a category.
• Review mining & sentiment — pull review text for LLM sentiment scoring, pain-point extraction and positioning.
• Reputation monitoring — track review volume and rating for your own or competitors' locations.
• Local market mapping — join coordinates to geographic/territory analysis.
• Email outreach lists — filter to businesses with a discovered email, then push straight to your CRM.
• Data enrichment — start from a link list and backfill structured business profiles.
• AI & LLM pipelines — JSON records for RAG, scoring, outreach drafts and territory summaries.
• Warehouse & BI feeds — scheduled runs feeding dashboards and data warehouses.

Scenario: restaurant lead list for a sales team

1. Enable Listing Discovery with markets restaurants / Austin, restaurants / Dallas.
2. Keep Lead details on, then filter by telephone/email in your CRM or spreadsheet after the run.
3. Run. Stream the dataset into your CRM, or set a webhook for real-time delivery.

Scenario: hotel competitor benchmark

1. Enable Listing Discovery with markets hotels / Las Vegas, hotels / Miami.
2. Compare rating, reviewCount and priceRange across the markets.

Scenario: review sentiment for a destination

1. Enable Reviews and paste business links.
2. Set maxReviewsPerPlace to 100 and feed the review text into your LLM.

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Output reference

Each dataset row is one record. Filter by featureType:

featureType	Description
listing	Business found through Listing Discovery
place	Business enriched from a specific link (Place Details)
review	A single review linked to a business

Traceability: marketLabel records which market row produced the business; sourceUrl is the business's Tripadvisor link.

Export formats: JSON, CSV, Excel, XML, RSS or via the Apify API.

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Input reference

Enable only what you need — all features are independent.

Input	Type	Default	Description
🏪 Listing Discovery
enableListingDiscovery	boolean	true	Discover businesses by category + market
listingTasks	object[]	2 example rows	{ category, location, maxItems? } per market
maxPagesPerTask	integer	20	How deep to go per market (safety ceiling)
enrichListings	boolean	true	Lead details — collect the full profile for every business, contact details included when published
📍 Place Details
enablePlaceDetails	boolean	false	Enrich specific business links
placeUrls	string[]	—	Tripadvisor business links
⭐ Reviews
enableReviews	boolean	false	Collect reviews for specific links
reviewUrls	string[]	—	Tripadvisor business links
maxReviewsPerPlace	integer	30	Max reviews per business
⚙️ Output & limits
maxItems	integer	1000	Global cap on dataset rows across all features
webhookUrl	string	—	Optional real-time POST URL (dataset is always written)
webhookFormat	enum	json	json (full record) or slack (Slack message)
🌐 Connection
proxyConfiguration	object	residential US	Apify proxy settings

Full schema: see .actor/input_schema.json or the Input tab on Apify Console.

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Webhook delivery (optional)

Every record is always saved to the Apify dataset first. If you set webhookUrl, each new record is also POSTed in real time to your endpoint — ideal for CRMs, Slack, Zapier, Make or custom pipelines.

Setting	Description
webhookUrl	Your endpoint (http/https). Leave empty to use the dataset only.
webhookFormat	json — full business or review object. slack — compact Slack incoming-webhook message.

Webhook delivery is best-effort: a failed webhook never stops the run or prevents dataset writes.

Example — discovery with Slack alerts

{
  "enableListingDiscovery": true,
  "listingTasks": [{ "category": "restaurant", "location": "Austin", "maxItems": 50 }],
  "webhookUrl": "https://hooks.slack.com/services/YOUR/WEBHOOK/URL",
  "webhookFormat": "slack"
}

Example — lead export with JSON webhook

{
  "enableListingDiscovery": true,
  "listingTasks": [{ "category": "restaurant", "location": "Chicago" }],
  "enrichListings": true,
  "webhookUrl": "https://your-crm.example.com/api/leads",
  "webhookFormat": "json"
}

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Quick start examples

Multi-market restaurant leads

{
  "enableListingDiscovery": true,
  "listingTasks": [
    { "category": "restaurant", "location": "Austin", "maxItems": 100 },
    { "category": "restaurant", "location": "Dallas", "maxItems": 100 }
  ],
  "enrichListings": true
}

Hotels across destinations

{
  "enableListingDiscovery": true,
  "listingTasks": [
    { "category": "hotel", "location": "Las Vegas", "maxItems": 50 },
    { "category": "hotel", "location": "Miami", "maxItems": 50 }
  ],
  "enrichListings": true
}

Attractions by geo ID (any market)

{
  "enableListingDiscovery": true,
  "listingTasks": [{ "category": "attraction", "location": "298184", "maxItems": 75 }]
}

Enrich a link list

{
  "enableListingDiscovery": false,
  "enablePlaceDetails": true,
  "placeUrls": [
    "https://www.tripadvisor.com/Restaurant_Review-g60763-d7890999-Reviews-Kuu_Ramen-New_York_City_New_York.html"
  ]
}

Review sentiment feed

{
  "enableListingDiscovery": false,
  "enableReviews": true,
  "reviewUrls": [
    "https://www.tripadvisor.com/Restaurant_Review-g60763-d7890999-Reviews-Kuu_Ramen-New_York_City_New_York.html"
  ],
  "maxReviewsPerPlace": 100
}

API quick start

curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "enableListingDiscovery": true,
    "listingTasks": [
      { "category": "restaurant", "location": "New York", "maxItems": 100 },
      { "category": "hotel", "location": "London", "maxItems": 100 }
    ],
    "enrichListings": true
  }'

Dataset items: GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json

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LLM & MCP integration

Output is JSON Lines–friendly structured data — ideal for ChatGPT, Claude, Gemini, LangChain, LlamaIndex and custom agents.

Recommended workflow

1. Run the Actor with the features you need.
2. Fetch dataset items via the Apify API or export JSON/CSV.
3. Pass records to your LLM with a system prompt, or index them into a vector store.

Example: one business for an LLM prompt

{
  "featureType": "listing",
  "name": "Kuu Ramen",
  "category": "restaurant",
  "address": "20 John St, New York City, New York, 10038, US",
  "telephone": "+1 212-571-7177",
  "emails": ["contact@example.com"],
  "rating": 4.6,
  "reviewCount": 308,
  "priceRange": "$",
  "cuisine": ["Japanese", "Asian", "Soups"],
  "url": "https://www.tripadvisor.com/Restaurant_Review-g60763-d7890999-Reviews-Kuu_Ramen-New_York_City_New_York.html",
  "marketLabel": "Restaurants | New York"
}

Apify MCP (Model Context Protocol)

Use the Apify MCP server so AI assistants can run this Actor from natural language, read its dataset directly in chat, and chain it with other Actors (enrich → score → outreach).

User: "Find highly rated restaurants in Austin with phone numbers and summarize each for outreach"
→ MCP runs the Actor with listingTasks=[{category:"restaurant", location:"Austin"}], enrichListings=true
→ MCP reads dataset items
→ LLM summarizes and drafts emails

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Connection & performance

• Apify's built-in residential proxy (US) is enabled by default — no extra setup.
• Default memory is 512 MB, enough headroom for large runs. Increase memory for very large multi-market jobs.
• Records are delivered to the dataset as they arrive, so long runs do not accumulate data in memory.
• A business that cannot be loaded is skipped, so one bad record never stops the run.

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Free plan & paid plans

This Actor is designed for paid Apify plans. Free-plan runs never crash or return a system error — they finish cleanly and explain the limit.

Plan	What you get
Free plan	A small sample of up to 2 results per run, plus a clear log message inviting you to upgrade.
Paid plan (Bronze and above)	Full, uncapped output with every feature.

The restriction is always stated transparently: the run log shows the limit and the run summary (OUTPUT) reports the paywall state (detected, isPaying, pricingTier, limited), so the upgrade path is visible without any guesswork.

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Limitations & compliance

• Data is limited to what Tripadvisor publicly displays for each business.
• Contact emails are returned only when a business publicly exposes one; not every business has an email.
• Some markets may return fewer businesses than requested.
• Not affiliated with, endorsed by, or sponsored by Tripadvisor. Use responsibly and comply with applicable laws and Tripadvisor's Terms of Service. Always respect rate limits and local regulations when collecting business data.

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FAQ

I got fewer results than maxItems. maxItems is a ceiling. Each market also has its own maxItems and maxPagesPerTask cap, and some markets list fewer businesses. Filtering to only businesses that have a phone, email or website happens on your side after the run — every business is always included in the dataset.

Why is a business missing an email? Emails are only returned when the business publicly publishes one. Keep Lead details on (enrichListings: true) for the best chance.

Can I collect from a city that isn't in the common list? Yes — use its numeric Tripadvisor geo ID, or paste the market's Tripadvisor link. Common city names are supported out of the box.

Does the webhook replace the dataset? No. The dataset is always written. The webhook is an additional real-time push.

Can I run this on a schedule? Yes. Create an Apify Schedule with your saved input and results land in a fresh dataset each run.

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Contact & custom work

Need something beyond this Actor? I build custom scrapers, data pipelines, and full-stack web applications for startups and enterprises.

• Email: dubem115@gmail.com
• GitHub: github.com/DrunkCodes

Reach out for:

• Custom Apify Actors (any website or data source)
• Tripadvisor / hospitality / local lead-gen data projects at scale
• LLM & MCP integrations with your data stack
• Web apps, dashboards and automation tools

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Tripadvisor Real-Time Data · by DrunkCodes