app-store-scraper-mcp
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- AlicenseNot gradedqualityDmaintenanceProvides live App Store rankings, charts, app details, reviews, and search across 55 countries, enabling AI assistants to query app store data.18 npmMIT
- AlicenseNot gradedqualityDmaintenanceEnables searching the App Store, retrieving app details, discovering trending apps, and accessing screenshots and release information via Apple's public APIs.4 npm4MIT
- AlicenseAqualityAmaintenanceGives AI agents live access to Apple App Store reviews: search the App Store, pull customer reviews for any iOS app, and turn them into insights like rating distribution, average rating per version, top complaint keywords, representative quotes, and side-by-side competitor comparisons.41MIT
- AlicenseCqualityDmaintenanceEnables querying and retrieving data from App Store and Google Play Store, including app details, reviews, ratings, rankings, permissions, and search capabilities across both iOS and Android platforms.20MIT

@sonarapp/mcpofficial
AlicenseAqualityCmaintenanceProvides App Store Optimization tools for AI agents, enabling app lookup, keyword research, ASO audit, review mining, and revenue estimation across iOS and Google Play.43123 npmMIT- AlicenseAqualityDmaintenanceEnables AI assistants to manage Apple App Store Connect resources like apps, builds, TestFlight, and reviews through natural language.209 npmMIT
TDQS
Scored across 9 tools
Each tool maps to a distinct data facet (search, charts, details, ratings, privacy, reviews, similar apps, developer catalog, autocomplete), so an agent can pick correctly in most cases. The only real overlap is that `app_details` already returns rating/version info that `app_ratings` surfaces separately, and `search_apps` vs `search_suggestions` vs `list_apps` require reading descriptions to keep straight.
All names are lowercase snake_case and semantically transparent, but they follow three different patterns: verb_noun (`search_apps`, `list_apps`), resource_aspect (`app_ratings`, `app_privacy`, `app_details`, `app_reviews`), and qualifier_noun (`similar_apps`, `developer_apps`). Readable and predictable enough, with only minor style drift rather than true inconsistency.
Nine tools is well within the sweet spot and each covers a genuinely different App Store data surface. No tool feels redundant filler or obviously missing from the core set.
The surface covers the main read-only App Store research workflow: autocomplete, search, charts, details, ratings, reviews, privacy labels, similar apps, and developer catalogs. Minor gaps remain, such as app version/changelog history, in-app purchase listings, and explicit storefront/country selection, but agents can work around them.