storelift-mcp
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- AlicenseAqualityAmaintenanceConnects App Store and Google Play keyword data to AI assistants, enabling ASO tasks like rank tracking, keyword analysis, competitor visibility, and top charts through natural language.3089 npmMIT

AppmanAi MCPofficial
AlicenseNot gradedqualityBmaintenanceEnables AI assistants to query live App Store and Google Play data — keyword demand, rank history, top charts, competitor sets, reviews and ratings — across 100+ countries, directly inside a chat instead of a dashboard. Exposes 35 read-only tools covering app search, ASO keyword profiling, ranking trajectories, and worldwide market comparisons through a remote OAuth-authenticated endpoint.3MIT- AlicenseNot gradedqualityBmaintenanceEnables AI clients to query live App Store and Google Play keyword rankings, review rank history and daily/weekly reports, research keyword popularity and difficulty by country, and inspect or track competitors' keywords in natural language. It also allows adding apps and tracking or untracking up to 200 keywords per app and country, with read-only and write operations governed by license role and plan limits.2MIT
- AlicenseNot gradedqualityBmaintenanceEnables retrieval of App Store and Google Play reviews, ratings summaries, and ASO keyword rankings for any app using its store ID or package name.219 npmMIT
- AlicenseNot gradedqualityAmaintenanceApp Store and Google Play ASO: keyword rankings, competitors, reviews, audits and an action queue.113AGPL 3.0

ASO Atlas MCP serverofficial
AlicenseNot gradedqualityBmaintenanceEnables AI assistants to research App Store keywords with popularity and difficulty scores, track daily rankings across 59 storefronts, find competitor keyword gaps, optimize metadata, and review App Store Connect performance. Read tools only inspect the signed-in account, while write tools manage tracked apps, keywords, competitors, and saved lists mid-conversation.MIT
TDQS
Scored across 85 tools
Descriptions are unusually explicit and often cross-reference other tools (list_apps first, confirm_action, get_job), so many boundaries are documented. However the 85-tool surface contains several adjacent analytics families (get_keywords/get_history/get_keyword_metrics/hive_keyword_metrics, get_reviews/hive_app_reviews/search_reviews, market_charts/get_charts), so an agent can still misselect without careful reading.
Nearly all names are lower snake_case and follow namespace-like prefixes (get_, list_, add_, remove_, hive_, market_), which is readable and predictable. But the set is not a uniform verb_noun convention: it mixes bare verbs (search, fetch, whoami), noun-prefixed families (market_charts, hive_app_details), and many different verb choices.
85 tools is an extreme mismatch for a single MCP server; the calibration rule treats 50+ as a 1. Even though many tools map to real ASO/ad workflows, the count alone forces catalog browsing and substantially increases selection errors.
The surface covers a huge domain: tracked apps/keywords, rival analysis, reviews, listing drafts/writes, Apple Ads commands, market charts, Hive data, alerts, revenue and job/confirmation flows. Some lifecycle gaps remain, such as no remove_app/untrack app, no list/update_alert_rule, and no general app tracking settings update, but most core workflows are covered.