Get the ASO Health audit
get_aso_healthGet the full ASO Health audit of one tracked app: how well its listing is WRITTEN, as opposed to how it currently ranks. Returns app (id, app_id, title, store, country, lang) so a score is always attributable, global (0-100), the three axes, the maturity tier with its label, every field, the terms it targets, flags[], and timeline[] of past releases with their score. THE AXES: coverage = how much searched vocabulary the indexed fields carry; targeting = whether the terms it aims at are winnable AT THIS APP'S SIZE; appeal = rating, screenshots and freshness. TIER drives targeting: 1 Emerging, 2 Growing, 3 Established. The verdict threshold moves with it, so the same term can be "ambitious" for a tier 2 app and "out-of-reach" for a tier 1 one, and a tier 3 app can reasonably aim higher. Say that when you explain a verdict. fields[].state is "scored", "empty" (blank on the store) or "missing" (the iOS keywords field, which Apple never publishes: we only know it if the user gave it to us). fields[].key stays "subtitle" on Google Play, where the store calls that field the short description: use the store's name when you talk to the user. fields[].text is TRUNCATED to a preview and fields[].truncated says so; the full text is on list_applications. targeting.keywords[].verdict is "reachable", "ambitious" or "out-of-reach"; traffic and difficulty are both 0-100. targeting.ungraded counts searched terms we have no competition data for; targeting.unsearched counts words nobody types, listed in unsearched_words. phrases[] lists the searched PHRASES the listing forms across its indexed fields (text, traffic 0-100, words with the field each comes from): a word nobody types alone still earns coverage when it completes one, and removing it breaks the phrase. targeting.assessable is false when the listing holds no searched term at all, and then there is no targeting to judge. report is null on an app the audit has never reached, which is not a score of zero. The audit is kept up to date automatically, so this reads a stored result and answers in milliseconds: call it as often as you need. For visibility over time instead, use get_app_score_history; to score a listing that does not exist yet, use simulate_metadata.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The application INTERNAL id: the "id" field of list_applications (numeric, e.g. "344"). Not its "app_id" field, which is the store bundle id. | |
| include_words | No | Add the per-word table of every indexed field: name, traffic (0-100), difficulty (0-100), occurrences and status. STATUS values: "earning" (real search demand, and this field is credited for it), "already-used" (an earlier field already claimed it; the stores index a word once), "not-searched" (indexed, but nobody types it here), "in-phrase" (nobody types it alone, but it completes a searched phrase, named in its "phrase" with the traffic of that phrase; it earns coverage like a keyword), "pending" (no search data yet on this storefront), and on the Google Play long description "covered" / "missing" (does it repeat what the title and short description target) and "extra-reach" (a searched word it carries that no other field does). occurrences is only counted on the Google Play long description, where repetition is visible; it is null elsewhere, because a word is either in the field or not. Off by default: it is detail you rarely need to answer how a listing is doing. |