Applyra

Deep-analyze any keyword (even ones you don't track). Returns difficulty_score (0-100), traffic_score (0-100), KEI with score and level (e.g. "good"), the top 20 apps currently ranking for it (with rank, app_id, title, icon, genre, rating), related keyword suggestions from search and keyword sources, and whether you already track this keyword. Inspecting a keyword you already track does not consume the inspection quota.

inspect_keyword

List all tracked mobile applications. Returns each app with its store metadata: title, description, app_id (bundle ID), store (ITUNES or GPLAY), country, lang (BCP-47), icon URL, screenshots, developer name, genre, version, rating score, number of ratings, the count of tracked keywords, and aso_health (the listing audit: global score plus its coverage, targeting and appeal axes, or null on an app the audit has not reached yet; get_aso_health returns the breakdown behind it). Usually the first call of a workflow: the numeric internal ID it returns is what track_keywords, add_competitor, get_app_score_history and the other app-scoped tools expect, whereas add_application takes the store bundle ID instead. Read-only.

list_applications

List the autocomplete queries you have previously run, with the prefix, store, country, lang, suggestion count and last query date.

list_autocomplete_history

List competitor tracking pairs. Each pair holds your app (id, app_id, title, icon, store only) and the competitor app with its full store metadata (title, description, url, icon, screenshots, developer_name, genre, version, score, ratings), plus both apps' visibility scores (app_score vs competitor_score, 0-100) for direct ASO comparison. Use list_applications to get the full metadata of your own app.

list_competitors

List the keywords you have previously inspected, with their last inspection date and current difficulty/traffic scores. Useful to revisit past keyword research without consuming the inspection quota again.

list_keyword_inspections

List the listing drafts already scored on this account, newest first, whether they were run from here or from the Metadata Simulator in the dashboard. Each row holds the draft TITLE ONLY, its market, the score it reached with the three axes, the app score it was measured against, whether it differed from the live listing, and how many times it has been re-run. It does NOT carry the subtitle, keywords field or description: pass a row's "id" to get_metadata_simulation to read a draft in full. Use this to find a past score without re-running it.

list_metadata_simulations

List the niche analyses previously run, with topic, store, country, lang, cluster count, keyword count, top opportunity score and creation date. Paginated through page and per_page. Read-only: it re-reads work already done, so call it before run_niche_analysis to check whether a topic was already covered. It returns summary rows only, not the clusters and keywords themselves.

list_niche_analyses

List the categories and collections supported by the top_charts tool, per store. Use a returned category "key" (e.g. "OVERALL" for the overall chart, or a store value like "6014" / "GAME") and a collection (free, paid, grossing) as inputs to top_charts.

list_top_chart_categories

List tracked keywords with ASO metrics. Returns each keyword with: keyword text, store, country, lang, difficulty_score (0-100), traffic_score (0-100), current_rank, ahead/behind (the apps ranked immediately above and below yours), the top 5 apps ranking for this keyword (in ranking order, rank 1 to 5), is_favorite flag, and the tracking start date. current_rank comes from the latest daily ranking snapshot and is null when the app is not in the top 100 for that keyword, in which case ahead and behind are null too. A null rank always means "not ranked", never "unknown": the tool errors out rather than returning partial rank data. Paginated.

list_keywords

Mark or unmark a tracked keyword as favorite for a specific app. Favorites are stored per tracking row (profile + app + keyword), so a keyword tracked across multiple apps has independent favorite states. Both keyword_id and app_id come from list_keywords results.

set_keyword_favorite

Remove a competitor relationship by its internal relation ID (the `id` field returned by list_competitors or add_competitor — note that this is the relation row id, not the competitor app id).

remove_competitor

Run a niche analysis on a topic: discovers relevant keywords, clusters them into sub-niches, and scores each cluster's opportunity. Returns clusters with their keywords, opportunity scores, intent type, and an app concept suggestion. Cache hits (recent identical analyses, less than 7 days old) are returned instantly without consuming the niche analysis quota. Fresh analyses can take a few minutes.

run_niche_analysis

Score a listing that does not exist yet, on the same engine that audits the real ones, and see the gain or loss against the app's current score. WITH app_id: every field you leave out is read from that app's live listing AND its real context (rating, screenshots, age, languages), so { app_id, title } is a complete request and the delta is meaningful. This is the mode to use to answer "is my draft better than my listing". WITHOUT app_id: you must pass store, country and lang, and the context defaults to a listing NOBODY HAS PUBLISHED: no rating, no screenshot, one language. That deliberately floors the appeal axis, so the global score is NOT comparable to a real app's score and you must not present it as one. Compare the coverage axis instead, or pass `context` yourself to describe the app you have in mind. `context` works in both modes: any key you send overrides, any key you omit keeps the app's value (or the blank default). Returns app (null without app_id), aso_health with the three axes and the tier, delta against the app's stored score (null for a draft from scratch, and each axis is independently null when it was never computed), flags[], the saved run, and notes[] for anything wrong with the request that did not stop it (a kw_field sent for a Google Play listing, which has none, is ignored and reported there). words_probed is how many words the run weighed; words_fetched is how many of those had to be read from the store for the first time. words_fetched: 0 means everything was already known and the call was fast; a high one is why a call took several seconds, and it warms the cache for everyone afterwards. Expect a few seconds per call. Check the draft with check_metadata first, and change something meaningful between two calls rather than polling it. Re-read a past run in full with get_metadata_simulation, or list them with list_metadata_simulations.

simulate_metadata

Track up to 20 new keywords for one of your applications in a single call. Each keyword triggers an immediate ranking fetch. Already-tracked keywords return a per-keyword error in `results`; previously removed keywords are reactivated automatically. Use app_id from list_applications results.

track_keywords

Stop tracking a keyword for the specified app (soft delete). The historical ranking data is preserved; re-adding the same keyword reactivates the row. Idempotent: calling on an already-removed row returns success.

untrack_keyword