App Store Reviews Scraper — All Storefronts, No 500 Cap

Extract Apple App Store reviews for any app, from every country storefront, in one run.

The problem this solves

Apple's review feed hard-stops at 500 reviews per country (10 pages × 50). Every other App Store scraper hits that wall and stops, so you get 500 reviews and a partial picture.

This Actor sweeps all ~165 storefronts and merges the results.

Real benchmark — Facebook (id284882215), a single run:

	Single-country scraper	This Actor
Reviews returned	500	21,457
Countries covered	1	74
Oldest review	recent only	2012
Run time	—	46 seconds

That is 43× more data, spanning 14 years of review history.

What you get

One record per review, flat and ready for a spreadsheet, database, or LLM:

{
  "type": "review",
  "reviewId": "14457694417",
  "appId": "284882215",
  "country": "US",
  "rating": 1,
  "title": "The pay",
  "text": "They should pay more!",
  "author": "Lost lady 90",
  "appVersion": "575.0.0",
  "voteSum": 0,
  "voteCount": 0,
  "updatedAt": "2026-08-22T00:00:29-07:00",
  "scrapedAt": "2026-08-23T11:45:49.941Z"
}

Plus one type: "app" record with name, developer, version, price, overall rating and total rating count.

Results are de-duplicated by review ID across storefronts — verified 0 duplicates across 21,457 records.

Input

Field	Description
appId	Numeric ID (284882215) or any App Store URL. Both work.
countries	["all"] for maximum coverage, ["default"] for 51 major markets, ["tier1"] for the 20 largest, or explicit codes like ["us","gb","de"].
sortBy	mostRecent or mostHelpful.
maxReviews	Hard cap across all storefronts. This is your cost control — the cap is exact.
maxPagesPerCountry	1–10. Lower it for a fast, cheap sample across many countries.
minRating / maxRating	Set both to 1 to pull only 1-star reviews.
sinceDate	ISO date, e.g. 2026-01-31.
keywords	Keep only reviews mentioning these words.

Example: every 1-star review worldwide, this year

{
  "appId": "284882215",
  "countries": ["all"],
  "minRating": 1,
  "maxRating": 1,
  "sinceDate": "2026-01-01"
}

Example: track one bug across major markets

{
  "appId": "284882215",
  "countries": ["default"],
  "keywords": ["crash", "crashing", "freeze"],
  "sinceDate": "2026-06-01"
}

What people use it for

• ASO and competitor research — read what a rival's users complain about in markets you're entering, not just the US.
• Localization QA — 1-star reviews cluster by country when a translation or payment method is broken. One storefront's reviews will not show you this.
• Release monitoring — filter by appVersion to see whether the complaint rate moved after a release.
• Feeding an LLM — the flat JSON drops straight into a summarization or clustering pipeline. Ask it what the top 10 complaints are per market.
• Due diligence — 14 years of review history is a real signal about an app's trajectory.

Pricing

$0.60 per 1,000 reviews, plus a negligible per-run start fee.

For comparison, the nearest App Store review Actors charge $1.50–$2.00 per 1,000 — and return 500 reviews maximum.

Use maxReviews to set a hard ceiling on any run.

Reliability

This Actor uses Apple's own public RSS and Lookup endpoints. There is no login, no cookie, no API key, and no anti-bot layer — which means no proxy costs, no breakage when a site ships a new front end, and no silent failures. It is about as stable as a scraper gets.

Known limits, stated plainly:

• Apple caps each storefront at 500 reviews. This Actor works around that by sweeping many storefronts; it cannot exceed 500 within one country.
• Not every app is published in every storefront. In the Facebook benchmark, 74 of 165 storefronts returned data — the rest legitimately have none.
• Review text is returned in whatever language the reviewer wrote. Translation is not included.

Use it from an AI agent

The Actor is exposed over MCP, so Claude, Cursor, or any MCP client can call it directly:

"Pull every 1-star review for app 284882215 from the last 3 months across all countries and tell me the top 5 recurring complaints."

Notes on data

Reviews are public content published by Apple. Author names are the public display names shown on the App Store. If you process this data for users in the EU or UK, that is still personal data under GDPR — have a lawful basis, and do not use it to build contact lists.