AppmanAi

List every category chart an app currently appears in, with its rank in each. The place to start when you do not already know which chart matters. Costs 3 credits.

An app's position in a category chart across past days — the series to chart when asking whether an app is climbing or sliding. Costs 6 credits.

An app's current position in a single category chart. Use it when you know exactly which chart you mean; app_category_rankings lists every chart the app appears in. Costs 2 credits.

For each app given, the rank it first held in a chart from a start date — the baseline to measure a campaign against. Costs 5 credits.

An app's total installs worldwide, at most one point per day (days without data are absent), with daily — the gain per day since the previous point (null on a first point with no earlier data and on a day the total fell). For growth over the window, subtract the first total from the last. Worldwide only: there is no per-country figure. Google Play figures are Google's exact install count, and total_bucket is the range its store page shows. Apple publishes no install counts, so App Store figures are an estimate: approximate for any single app, most reliable as a trend over time or between similar apps, and to be presented to the user as an estimate. Pick the window with from / to; the last 30 days by default. Costs 10 credits.

Fetch the full store listing for one app (title, developer, ratings, category, metadata, whether it shows ads or sells in-app purchases, and their price range and the purchase items the store lists with prices) by its package id. If you only have the app name, call app_search first to resolve it. Costs 3 credits.

Fetch store listings for a list of package ids in one call — the bulk form of app_metrics_metadata. Use it when comparing apps, instead of one call per app. Costs 8 credits.

An app's average rating and star breakdown over time in one country — a dated series for charting whether a release helped or hurt. app_metrics_ratings_worldwide gives the current picture across all countries instead. Costs 5 credits.

An app's current rating and per-star vote counts in every country it is listed in. Use it to see where an app is rated well or badly; app_metrics_ratings_history tracks one country over time. Costs 6 credits.

An app's estimated revenue from store purchases over the last 30 days: worldwide gross (what users paid) and net (what the developer keeps after the store's commission), each with a low–high range and a size bucket, plus the split by country, largest first. Neither store publishes revenue, so these are estimates on both stores: approximate for any single app, best for sizing an app or comparing apps, and to be presented to the user as estimates. An app the store lists no purchases for reads 0. Figures are in USD unless currency is given. Costs 10 credits.

Read real user reviews of an app: author, star rating, text and date, 40 per page. Use it to find out what users actually complain about or praise. On Google Play, page through in order (1, 2, 3…) while has_more is true, each page within about an hour of the previous one and with the same sort_type; skipping ahead fails with PAGE_EXPIRED (not charged), so start again from page 1. The App Store serves a single page: there is no page 2 on ios. Costs 4 credits.

Search a store for apps matching a name or phrase. Returns matching apps with their package ids — the way to resolve a name into a package id for the other tools. Costs 2 credits.

List the apps the store itself considers similar to a given app (title, developer, icon). The quickest way to build a competitor set from one seed app. Costs 5 credits.

List the apps at the top of a category chart (top free, top grossing, top paid) in a country. Use it to see who leads a category; list_categories has the valid tags. Costs 5 credits.

Rewrite an app store listing — title, short and long description — optimised for the keywords you pass and the store it targets. Takes the current copy directly, not a package id, so you can iterate on drafts; use app_metrics_metadata first if you want to start from a live listing. Costs 15 credits.

Generate a keyword list for an app from its own store listing, each scored with its popularity (SAP) and the app's current rank where it has one. Use it to propose new keywords to target; keyword_app_suggestions reports what the app already ranks for. Costs 12 credits.

List keywords that the competitors of one app rank for, with their popularity (SAP) and that app's own rank where it has one. Use it to find the gaps — keywords rivals own and this app does not. keyword_app_suggestions covers what the app itself already ranks for. Costs 6 credits.

Where one app currently ranks in search results for one keyword. The direct answer to "am I ranking for this?". A rank of 0 means the app is not in the results. Costs 2 credits.

One app's rank for a list of keywords in a single call. Use it to check a whole keyword set at once instead of calling keyword_app_metrics_rankings repeatedly. A rank of 0 means the app is not in the results. Costs 9 credits.

One app's rank for a keyword over time — the series to chart when asking whether a change helped. Pick the window with period. A rank of 0 means the app is not in the results. Costs 6 credits.

One app's rank for a keyword in every country at once. Use it to find the markets where the app already ranks, before spending calls per country. A rank of 0 means the app is not in the results. Costs 12 credits.

Given a set of apps, list the keywords they compete on, each with its popularity (SAP) and every app's rank for it. Use it to compare a whole competitor set head-to-head on one keyword list; keyword_app_competitors starts from a single app instead. Costs 7 credits.

List the keywords a specific app is recommended for / ranks on in a store/country, by its package id. The direct way to answer "what keywords does this app rank for?". If you only have the app name, call app_search first to resolve it. A rank of 0 means the app is not in the results. Costs 6 credits.

Get the ASO profile of a keyword: search-ads popularity (SAP), difficulty, and languages. Use it to judge whether a keyword is worth targeting. Costs 4 credits.

Get just the search-ads popularity (SAP, 0-100) of one keyword in a country, across both stores. The cheapest popularity check; keyword_metrics adds difficulty and languages. Costs 4 credits.

Get the search-ads popularity (SAP) of many keywords in one call. Use it to rank a whole keyword list by demand before profiling the promising ones. Costs 9 credits.

How much the store's ranking algorithm moved in one country over a period. High volatility means rank changes are the store's doing, not yours — check it before blaming a release. Costs 8 credits.

Ranking-algorithm volatility for one date across every country, so you can tell a global store change from a local one. Costs 12 credits.

Expand a seed keyword into related keyword suggestions for a store/country. Use it to grow a keyword list before profiling or checking ranks. Costs 5 credits.

Expand a seed keyword into suggestions with their popularity (SAP), across all markets at once and without picking a store or country. The cheap first pass before narrowing down with keyword_suggestions or keyword_metrics. Costs 10 credits.

List the apps that rank highest for a keyword in a store/country — the search results a user sees. Use it to find the incumbents / competitors for a keyword. Costs 5 credits.

List a store's category tree — category tags and the charts under each (top free, top grossing, top paid). Use its tags for category in the category tools and category_tag in generate_app_metadata; they differ by store. Free — spends no credits.

List the countries AppmanAi covers, with languages and per-store support levels. Use it to confirm coverage or resolve a country code before other calls. Free — spends no credits.

List which parts of the dataset are available for a store and country. Use it to check whether a kind of data exists for a market before spending credits looking for it. Free — spends no credits.

List every AppmanAi tool with what it costs in credits, plus the current balance. Answers "what can you do?" and "what will this cost?". Free — spends no credits.

Summarise this account's recent AppmanAi usage: credits spent, request count and a per-route breakdown over a window of days. Free — spends no credits.

Show the credit balance on the connected AppmanAi account, split into bonus credits (with their expiry), the monthly allowance and credits that never expire. Free — spends no credits.