# App Store Intelligence — iOS App Metadata & AI (iTunes) (`fuze/app-store-intelligence`) Actor

Look up any iOS app or developer via Apple's public iTunes API: ratings, rating count, category, price, version history, size and release dates — with an AI read on positioning and momentum. For app market research and competitor tracking.

- **URL**: https://apify.com/fuze/app-store-intelligence.md
- **Developed by:** [Fredrik Hansson](https://apify.com/fuze) (community)
- **Categories:** Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $44.00 / 1,000 app analyzeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## App Store Intelligence — iOS App Metadata & AI (iTunes)

**Point it at any iOS app and get the full picture in one row.** Hand this Actor a list of app names, numeric App Store IDs, or App Store URLs. For each one it queries **Apple's public iTunes API**, pulls the live metadata — ratings, rating count, category, price, version, size, release dates — and layers an **AI read** on top: how the app is positioned and whether it has momentum.

You bring the apps. It hands back a competitor sheet.

> This Actor reads **Apple App Store (iOS)** data only, via the official public iTunes API. No scraping, no login, no API key for the data source.

![App Store Intelligence — iOS App Metadata & AI (iTunes) — input and sample output](https://datatools-hub.vercel.app/actor-shots/app-store-intelligence.png)

### Quick start

1. **Fill in the input** — set the fields in the input tab (prefilled with a working example).
2. **Click Start** — the Actor runs and writes one record per item to the dataset.
3. **Get your data** — export to **JSON, CSV or Excel**, or pull it via the API.

***

### The flow

```
1. Give app names / IDs / URLs      2. Apple iTunes API resolves          3. Get finished intelligence
   (e.g. "Notion", "Spotify",   →       each one (lookup or          →      metadata · ratings · version
    1459969523)                          search)                             history · size · AI read
```

1. **List the apps** you care about — plain names (`Notion`), numeric App Store IDs (`1459969523`), or full App Store URLs. Mix and match.
2. **The Actor resolves each one** against the iTunes API: numeric IDs and URLs go through `lookup`, names go through `search` (best match first).
3. **Run it.** Every app comes back as one structured record — metadata plus an AI positioning + momentum read — ready for a spreadsheet or your CRM.

***

### What you get per app

For every app, the Actor:

1. **Resolves the app** via the Apple iTunes API — a numeric App Store ID or an App Store URL (it extracts the `id…` segment) goes through `lookup`; a plain name goes through `search` and takes the best match.
2. **Pulls the core metadata:** `appName`, `appId`, `developer`, `category`, `genres`, `price`, overall `rating` + `ratingCount`, current-version `currentVersionRating` + `currentVersionRatingCount`, `version`, `lastUpdated`, `releaseDate`, `fileSizeMB`, `contentRating`, `languagesCount`, screenshot count, supported-device count, and the store `url`.
3. **Computes signals:** `ageYears` (from first release), `daysSinceLastUpdate` (from the current version's release date), and `ratingGap` (current-version rating vs overall — a positive or negative shift).
4. **Keeps the raw text** too — the app's `description` and latest `releaseNotes` (trimmed).

Then the **AI layer** (OpenAI `gpt-4o-mini`, a fast and low-cost model) adds, grounded **only** in the data above:

- **`positioning`** — one sentence: the category and how the app is positioned, read from its name and description.
- **`momentumSignal`** — one sentence combining update recency, rating volume, and whether the current version is rated higher or lower than the app overall.
- **`strengths`** — concrete positives.
- **`concerns`** — concrete risks: stale updates, thin ratings, a rating drop in the current version.
- **`summary`** — two sentences.

***

### Three ways people use it

- **App market research** — size up a category fast. Use the **iTunes API** to pull **app metadata** and **iOS app ratings** across a shortlist, and let the AI read tell you which apps actually have momentum versus which are coasting on an old release.
- **Competitor app tracking** — drop your rivals' App Store IDs in and re-run on a schedule. Every row is a snapshot of ratings, version, and last-update date, so **app competitor analysis** becomes a diff over time: who shipped, whose rating slipped, who went quiet.
- **ASO / portfolio monitoring** — watch your own and client apps. Catch a `ratingGap` turning negative after a release, or a `daysSinceLastUpdate` creeping up, before it shows up in the charts.

Keywords this Actor covers: **App Store API**, **iTunes API**, **app metadata**, **iOS app ratings**, **app competitor analysis**.

***

### Input

| Field | Type | Notes |
|---|---|---|
| `apps` | array | **Required.** App names to search, numeric App Store IDs, or App Store URLs. One per line. |
| `country` | string | Two-letter App Store storefront code. Default `us`. |
| `searchLimit` | integer | Results to consider per **name** search. `1` = best match only. Default `1`. |
| `maxResults` | integer | Cap how many apps to analyze. Default `200`. |
| `maxConcurrency` | integer | Apps processed in parallel. Default `5`. |
| `model` | string | Override the OpenAI model. Default `gpt-4o-mini`. |

#### Minimal input example

```json
{
  "apps": ["Notion", "Spotify", "1459969523"],
  "country": "us"
}
```

***

### Output example

```json
{
  "input": "Spotify",
  "appName": "Spotify: Music and Podcasts",
  "appId": 324684580,
  "developer": "Spotify",
  "category": "Music",
  "genres": ["Music", "Entertainment"],
  "price": "Free",
  "rating": 4.8,
  "ratingCount": 29500000,
  "currentVersionRating": 4.8,
  "currentVersionRatingCount": 29500000,
  "version": "9.0.10",
  "lastUpdated": "2026-10-01",
  "releaseDate": "2011-07-14",
  "ageYears": 15.2,
  "daysSinceLastUpdate": 9,
  "ratingGap": 0,
  "fileSizeMB": 162.4,
  "contentRating": "12+",
  "languagesCount": 62,
  "url": "https://apps.apple.com/us/app/spotify-music-and-podcasts/id324684580",
  "positioning": "A free, ad-supported Music app positioned as an all-in-one streaming home for songs and podcasts.",
  "momentumSignal": "Strong momentum: updated 9 days ago with a massive rating base of 29.5M and a current-version rating level with its 4.8 overall.",
  "strengths": ["Enormous, high rating base", "Updated within the last two weeks", "Broad language support"],
  "concerns": [],
  "summary": "Spotify is a mature, free music and podcast streaming app with a huge and stable 4.8-star rating base. Frequent updates and a steady current-version rating point to healthy ongoing momentum."
}
```

Apps that can't be resolved come back with `success: false` and an `error` field (and null metadata), so a bad name never breaks the run.

***

### Pay-per-event pricing

This Actor uses **pay-per-event** billing with a single event:

| Event | Charged when | Price |
|---|---|---|
| `app-analyzed` | Once per iOS app looked up via the iTunes API and summarized with AI. | **$0.10** |

The Actor logs its OpenAI token usage and the estimated AI cost at the end of every run, so the event price always stays safely above the model cost.

***

### Data source

All app data comes from **Apple's public iTunes Search / Lookup API** (`itunes.apple.com`). It is a public endpoint — **no API key and no login** are required for the data source — and this Actor sends a browser User-Agent and respects reasonable rate limits. The data reflects the **Apple App Store (iOS)** for the storefront `country` you choose.

***

### Setup

The AI layer calls the OpenAI API. Set the environment variable / secret **`OPENAI_API_KEY`** on the Actor. The iTunes lookup itself needs no credentials.

***

### Use it from an AI agent (Apify MCP server)

This Actor is available through the [Apify MCP server](https://mcp.apify.com), so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of `apps` and read back the enriched App Store intelligence — with no custom integration code.

***

### Run it locally

```bash
npm install
## put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...
apify run -p
```

***

### Related actors

- [App Review Intelligence](https://apify.com/fuze/app-review-intelligence) — Turn App Store reviews into concrete product insights and competitor comparisons.
- [B2B Tech Stack Detector](https://apify.com/fuze/b2b-tech-stack-detector) — Detect the technologies a company runs, from a list of domains.
- [Google Reviews Insights (AI)](https://apify.com/fuze/google-reviews-insights) — Turn Google reviews into concrete insights for a business and its competitors.
- [AI Web Extractor](https://apify.com/fuze/ai-web-extractor) — Turn any URL plus a plain-English prompt into structured JSON.

# Actor input Schema

## `apps` (type: `array`):

App names to search, numeric App Store IDs, or App Store URLs. One per line.

## `country` (type: `string`):

Two-letter country code for the App Store storefront (e.g. us, gb, se).

## `searchLimit` (type: `integer`):

How many search results to consider per app name. 1 = best match only.

## `maxResults` (type: `integer`):

Max apps to analyze. One pay-per-event charge per app analyzed.

## `maxConcurrency` (type: `integer`):

How many apps to process in parallel.

## `model` (type: `string`):

Override the model (default gpt-4o-mini).

## Actor input object example

```json
{
  "apps": [
    "Notion",
    "Spotify"
  ],
  "country": "us",
  "searchLimit": 1,
  "maxResults": 200,
  "maxConcurrency": 5
}
```

# Actor output Schema

## `apps` (type: `string`):

All analyzed iOS app records in the default dataset.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "apps": [
        "Notion",
        "Spotify"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("fuze/app-store-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "apps": [
        "Notion",
        "Spotify",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("fuze/app-store-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "apps": [
    "Notion",
    "Spotify"
  ]
}' |
apify call fuze/app-store-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,fuze/app-store-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/zGLkGf0ST7wgTBfjE/builds/0G4oDhFRJlGrntThz/openapi.json