ASOLens

Resolves an app by id, App Store URL, or name and returns its current listing data plus the developer's other apps in this storefront.

Tests every word in YOUR OWN keywords field by searching a phrase where that word is the distinguishing one -- an anchor your app certainly covers, plus the word under test ('matemática' + 'escola'). Ranking for the probe means the word is working; not ranking means it is not earning its 100 characters. Searching each word alone instead would mostly measure how small your app is. READ THE SUMMARY FIRST: if almost nothing ranks, the answer is usually not 'fix these words' -- a live audit found 1 of 19 German probes ranking, with the app at zero ratings against competitors holding thousands, which means keyword work is not the lever in that storefront at all. Requires App Store Connect credentials (the keywords field is private).

Runs the keyword-field audit across several storefronts at once and puts them side by side: how much of your keyword field places in each, your rating count there, and the competitor median with its sample size. This is the 'which market should I work on' question, which no single-market answer can settle. Uses auto-picked anchors only -- to override an anchor or a probe, run audit_keywords for that one market. Requires App Store Connect credentials (the keywords field is private). READ THE ANCHOR COLUMN: each market's numbers come from a different anchor in a different language, so the medians are NOT commensurable and only large differences mean anything.

Runs two keywords in one storefront and reports how much their ranked results overlap. Use this BEFORE concluding anything from a rank difference between two queries: rank is relative to whoever else is in that result set, so an app at #19 for one phrase and #131 for another has not necessarily been hurt by the extra word -- the two searches may simply have different competitors. identicalResults: true means Apple answered one query for both (it does this for some accented/unaccented pairs, and for some rewordings), so any rank difference is noise; NULL means both queries returned nothing, so there was no list to compare and no conclusion to draw either way. A low overlap means the two are genuinely different searches and their ranks are not comparable at all.

Looks up an app by id, App Store URL, or name and returns candidate matches with rating and developer info.

Looks up where one app ranks for a list of keywords, in one or more storefronts, with day-over-day trend where available.

Builds a table of your saved keywords with current rank, difficulty and trend, refreshing today's data by default.

Fetches recent low-star reviews for an app so you can read what's actually bothering users; theme extraction is left to the caller.

Reads YOUR OWN app's indexed listing text -- name, subtitle and the keywords field -- from App Store Connect, per locale. This is the only way to see the keywords field: it is private to the developer and no public endpoint exposes it, so for anyone else's app it stays unavailable. Requires App Store Connect API credentials; without them the tool says so rather than failing. Use it before judging a keyword: coverage of a query's words across name+subtitle+keywords is what you can actually change.

Builds a competitive report across a set of keywords: the union of top-ranked apps, a weakness score per competitor, difficulty per keyword, and gaps for your own app.

Looks up DataForSEO search volume for keywords you already have, by naming each one to the vendor. Use this to RANK a list -- 'which of these 40 terms is biggest' -- which niche_report cannot answer, because it reads each competitor's top 50 keywords only and everything below that comes back unpriced. This asks across each app's full ranked list instead. Paid: keywords are sent in batches of 150, and each batch is $0.012 per app, plus $0.00012 per keyword actually found -- so 200 keywords across 2 apps is 4 batches, about $0.05. Free for the rest of the UTC day once bought. US storefront only, like all volume in this server. A keyword still comes back unavailable if none of the queried apps ranks for it -- volume exists only as 'the keywords this app ranks for', so the answer depends on which competitors are reached, and it is NEVER evidence a keyword is unpopular.

Ranked apps for one keyword in one country, as the App Store lists them (fetched from this machine; cached for the UTC day). Bundles are kept as ranked rows. resultCount is what Apple returned (250 is a ceiling), not how many apps compete.

Compares two named snapshots keyword by keyword: rank before and after (null included), how the result set size changed, which apps entered or left the top 10, and your ratings against the competitor median on each date. Rows are grouped by the labels given when saving. READ THE CONTROLS FIRST: if keywords you expected to hold still have moved, the whole comparison is drift and the targets prove nothing. Deliberately reports no significance or confidence -- with small numbers there is no statistical power, and a confidence label would be theatre.

Captures a named, kept measurement of a keyword set: rank (including null, which is a value here), resultCount, the top 10 app ids, keyword coverage with the listing version it was read from, and your rating count against the competitor median per storefront. Take one BEFORE shipping a metadata change -- the moment cannot be recovered afterwards, because Apple publishes no rank history and this tool's own cache is per-UTC-day. Label each keyword ('target', 'control', 'regression watch') so a later diff can tell signal from drift: if the controls move while nothing about them changed, the reading is noise.

Returns App Store autocomplete suggestions for a search term, showing what people actually search for -- the way to check a keyword's intent is what you assume (e.g. 'rakna' autocompleting to calorie counting, not maths). Suggestion order is Apple's own ranking. Autocomplete is PREFIX completion, so a count of zero means nothing popular extends the term -- NOT that nobody searches it ('math quiz for kids' returns none and is plainly a real search). Read a low count as a prompt to look closer, never as proof a keyword is dead. This tool never spends money. It cannot buy search volume (no vendor prices a bare App Store keyword), but a suggestion that a niche_report, keyword_table or price_keywords run already bought a price for in 'us', on any of the last 7 UTC days (today counted as the first), comes back priced for free in `volumes`, carrying the day the price was bought so an older number says it is older; when none of the suggestions has such a price, a single `volume` field says why.