App Store Review Scraper (Apple iOS)

Collect recent Apple App Store reviews for ASO research, product discovery, competitor monitoring, and customer feedback analysis. Provide App Store URLs, numeric Apple app IDs, or a mixture of both and receive one clean dataset row per review.

The Actor uses Apple's public iTunes Lookup and Customer Reviews RSS JSON endpoints. It needs no API key, paid proxy, browser, or third-party data subscription.

What it does

• Accepts Apple App Store URLs and numeric app IDs.
• Resolves app metadata including name, seller, rating summary, price, genres, and current version.
• Reads up to 10 pages of the most recent reviews per app.
• Exports review title, text, 1–5 rating, author, reviewed app version, and ISO timestamp.
• Retries temporary request failures and isolates errors so one bad app does not stop the run.
• Deduplicates repeated app IDs and review IDs.

Input

Field	Type	Required	Default	Description
apps	string array	Yes	WhatsApp URL and ID in the prefill	App Store URLs or numeric app IDs.
country	string	No	us	Two-letter App Store storefront country code.
maxReviewsPerApp	integer	No	100	Reviews saved per app, from 1 to 500.

Example input:

{
  "apps": [
    "https://apps.apple.com/us/app/whatsapp-messenger/id310633997",
    "310633997"
  ],
  "country": "us",
  "maxReviewsPerApp": 100
}

Both example values resolve to the same app ID, so the Actor processes WhatsApp once and does not create duplicate reviews.

Output

Each dataset item is one review:

{
  "appId": "310633997",
  "appName": "WhatsApp Messenger",
  "country": "us",
  "reviewId": "12345678901",
  "title": "Useful every day",
  "text": "Fast and reliable messaging.",
  "rating": 5,
  "author": "Example user",
  "version": "2.26.10",
  "updatedAt": "2026-01-15T12:30:00.000Z"
}

The app metadata lookup supplies the canonical app name and is logged with seller name, average rating, rating count, price, genres, and current version. The dataset stays review-focused with the stable fields shown above.

Data sources and limits

The Actor only calls official, public Apple endpoints:

• https://itunes.apple.com/lookup?id=<appId>&country=<country> for app metadata.
• https://itunes.apple.com/<country>/rss/customerreviews/page=<1..10>/id=<appId>/sortby=mostrecent/json for reviews.

Apple exposes at most 10 review pages through this feed. Apps without public reviews in the selected storefront produce no dataset rows. Availability and ordering are controlled by Apple and can vary by country.

Cost

There are no external data-source fees. On pay-per-event Actor pricing, one result event is charged for each review saved to the dataset.

How to use App Store Review Scraper (Apple iOS)

1. Open App Store Review Scraper (Apple iOS) on Apify Store and click Try for free.
2. Fill in the input (the form is prefilled with a working example) and click Start.
3. Watch results appear in the Output tab, then download them as JSON, CSV, Excel or HTML, or read them through the API.
4. Optional: save the input as a task and add a schedule to run it automatically, or connect it to your tools with integrations and webhooks.

Use App Store Review Scraper (Apple iOS) with AI agents (MCP)

Give Claude, ChatGPT, Cursor, VS Code or any MCP client this Actor as a tool through the hosted Apify MCP server. The agent sees the input fields with descriptions and example values, runs the Actor, and reads the results.

MCP server URL (this Actor only):

https://mcp.apify.com/?tools=technicaldost/app-store-review-scraper

Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com/?tools=technicaldost/app-store-review-scraper" --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Cursor, VS Code, Claude Desktop and other clients (mcp.json or the client's MCP settings):

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=technicaldost/app-store-review-scraper",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Clients that support sign-in (such as Claude.ai connectors) can use the URL without a token and log in to Apify instead. Get your token in Apify Console → Settings → API & Integrations, or generate a ready config for your client at mcp.apify.com. In the agent the tool is named technicaldost--app-store-review-scraper; ask it, for example, to "run App Store Review Scraper (Apple iOS) with {"apps": ["https://apps.apple.com/us/app/instagram/id389801252"], "country": "us", "maxReviewsPerApp": 20}".

AI agents without an Apify account can also run and pay for this Actor per request with x402 agentic payments.

Run App Store Review Scraper (Apple iOS) via API

Run the Actor and get its results in one call (replace YOUR_APIFY_TOKEN):

curl -X POST "https://api.apify.com/v2/acts/technicaldost~app-store-review-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"apps": ["https://apps.apple.com/us/app/instagram/id389801252"], "country": "us", "maxReviewsPerApp": 20}'

Python (pip install apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("technicaldost/app-store-review-scraper").call(run_input={"apps": ["https://apps.apple.com/us/app/instagram/id389801252"], "country": "us", "maxReviewsPerApp": 20})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript / TypeScript (npm install apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('technicaldost/app-store-review-scraper').call({"apps": ["https://apps.apple.com/us/app/instagram/id389801252"], "country": "us", "maxReviewsPerApp": 20});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Long runs: start them with POST /v2/acts/technicaldost~app-store-review-scraper/runs and fetch the dataset when the run finishes, or receive a webhook.

Integrations

App Store Review Scraper (Apple iOS) works with Make, Zapier, n8n, LangChain, Google Sheets, Slack, GitHub and webhooks. Schedule runs in Apify Console and send new results straight to your apps, spreadsheets or AI pipelines.

Related Actors

• App Store Review Analyzer: Feature Roadmap — Collect public Apple App Store review feeds or import reviews, then group feature requests, usability complain
• B2B SaaS Review Buyer Intent Monitor — Extract review-site complaints, alternatives, and buyer-intent signals for SaaS competitors
• Yelp Business & Review Scraper — Scrape Yelp business listings and reviews: name, category, address, phone, rating, review count and individual
• Competitor Review Analyzer: Brand Comparison — Compare imported customer-feedback records by brand to surface recurring complaints, strengths and product gap
• Steam Scraper — Games, Prices, Discounts & Reviews: Scrape Steam games and user reviews without an API key: prices, discounts, tags, genres, review scores, developers, release dates, plus full review text,…
• Udemy Scraper — Courses, Prices, Ratings & Reviews: Scrape Udemy courses by topic, category or URL: current sale price and list price, rating, students, reviews count, instructors, hours, level, language,…

FAQ

Can I use App Store Review Scraper (Apple iOS) with AI agents like Claude or ChatGPT?

Yes. Connect the Apify MCP server with ?tools=technicaldost/app-store-review-scraper (see above) and the agent can run the Actor and read its results on its own.

Can I call App Store Review Scraper (Apple iOS) from my own code?

Yes. Use the REST API, or the official Python and JavaScript clients shown above. Results are available as JSON, CSV, Excel, XML or HTML.

How do I run it on a schedule?

Save your input as a task in Apify Console and add a schedule (for example, daily). Use webhooks or integrations to send each run's results onward.

Is it legal to use this Actor?

It works with publicly available data. Make sure your use complies with the source website's terms and with data protection laws such as GDPR, especially for personal data.

Something doesn't work or I need a feature. What should I do?

Open an issue in the Issues tab on this Actor's page with an example input, and we'll look into it.