Apify Public Data & Leads

Apple App Store Reviews and App Metadata

app_store_reviews_search

Read-onlyIdempotent

Search Apple App Store customer reviews and app metadata across storefronts. Get star ratings, review text, version-level sentiment for ASO, competitor research, and release feedback.

Instructions

Search the Apple App Store and return customer reviews with star ratings, review text, the app version each review was written against, and full app metadata - across any storefront.

Behavioral Transparency:

Execution: Network call executed synchronously in the cloud via Apify Actor 'captainhandsome/app-store-reviews-search'.
Side Effects: Strictly read-only; reads Apple's public search, lookup and review endpoints.
Authentication: Requires APIFY_TOKEN environment variable.
Latency & Limits: Typical run duration is 5-30 seconds; Apple caps the public review feed at 500 reviews per app per storefront; timeout capped at 120 seconds.

Usage Guidelines:

When to use: Use for iOS app reviews and ratings, App Store metadata, release-version sentiment, ASO and competitor research, or to pair with Android data for a cross-platform view.
When NOT to use: Do not use for Android reviews (use 'google_play_reviews_search'), for employer reviews, for app download estimates, or for Mac-only titles.
Named alternatives: Use 'google_play_reviews_search' for the Android half of the same product, 'google_maps_search' for business reviews, or 'youtube_video_search' for video sentiment.

Input Schema

TableJSON Schema

Name	Required	Description
`app_ids`	No	Specific apps as numeric track IDs ('570060128'), bundle IDs ('com.duolingo.DuolingoMobile') or App Store URLs. Overrides search_query.
`countries`	No	Two-letter App Store storefronts to collect from, e.g. ['us','gb']. Reviews differ per storefront. Defaults to ['us'].
`max_rating`	No	Keep only reviews at or below this star rating, e.g. 2 for complaint mining.
`min_rating`	No	Keep only reviews at or above this star rating, e.g. 4 for positive quotes.
`max_results`	No	Maximum rows to return and bill across every app and storefront. Defaults to 25.
`recent_days`	No	Keep only reviews posted within this many days, e.g. 30 for the last month.
`search_query`	No	Find apps by name or keyword, as you would in App Store search (e.g. 'language learning'). Omit when app_ids is given.
`include_reviews`	No	Collect reviews as well as app metadata. Set false for a fast metadata-only survey of a category.
`max_reviews_per_app`	No	Upper bound on reviews per app per storefront. Apple's public feed caps at 500. Defaults to 100.

Output Schema

TableJSON Schema

Name	Required	Description
`run`	No
`error`	No
`status`	Yes
`results`	Yes	App Store reviews, or app-metadata rows when include_reviews is false.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.4.0Sep 18, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description goes further by detailing execution mode (network call via Apify Actor), side effects (read-only, hitting Apple's public endpoints), authentication requirements (APIFY_TOKEN), and concrete limits (5-30s latency, 500-review cap, 120s timeout). This adds valuable operational context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured into a concise main sentence followed by clearly labeled sections (Behavioral Transparency, Usage Guidelines). Every sentence adds value: the purpose is front-loaded, operational details are scannable, and usage rules are explicit. There is no fluff or redundancy, making it efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters fully described in the schema, a rich output schema present, and annotations covering read-only and idempotent behavior, the description adds the missing pieces: execution method, authentication, latency, limits, and routing rules. Nothing an agent needs to correctly invoke this tool is absent. It is complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with each parameter described (e.g., app_ids, countries, min/max_rating, recent_days). The description's main text does not add parameter-specific meaning, but the schema already provides it. The behavioral notes about the 500-review cap relate to max_reviews_per_app, offering marginal extra context. Baseline 3 is appropriate since the schema carries the semantic load.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Search'), a clear resource ('Apple App Store'), and enumerates the exact outputs: customer reviews with star ratings, review text, app version, and full metadata. It differentiates from sibling tools by naming alternatives like 'google_play_reviews_search' and 'google_maps_search' in the usage section, leaving no ambiguity about what this tool does versus its peers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use scenarios (iOS reviews, ASO, competitor research) and when-not-to-use cases (Android, employer reviews, Mac-only titles), plus named alternatives for each exclusion. This is textbook guidance that lets an agent decide correctly without needing to infer context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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