# Apple App Store ASO, Competitor Metadata & Review Miner (`lucacelli/apple-appstore-aso-competitor-review-intel`) Actor

Mobile App & Gaming intelligence Actor: extracts iOS App Store ratings, version velocity, developer websites, and mines recent user reviews to identify 1-2 star bug/complaint themes.

- **URL**: https://apify.com/lucacelli/apple-appstore-aso-competitor-review-intel.md
- **Developed by:** [Ömürsel Ocakcı](https://apify.com/lucacelli) (community)
- **Categories:** Marketing, Business, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.50 / 1,000 extracted ios app metadata & user review pain-point analysis (all-inclusive)s

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## Apple App Store ASO, Competitor Metadata & User Complaint Miner (MCP Ready)

> **Apple App Store Scraper | ASO Keyword Research & 1-Star Review Miner** — Mobile App & Gaming intelligence Actor: extracts iOS App Store ratings, version velocity, developer websites, and mines recent user reviews to identify 1-2 star bug/complaint themes.

***

### 🚀 Why Choose This Actor?

1. **Ultra-Low Compute Cost (No Headless Browser Overhead)**: Built on a high-efficiency HTTP & structured-data parsing engine instead of heavy Chromium instances. Runs **10x faster** and consumes minimal Apify platform RAM/CPU.
2. **Transparent Pay-Per-Event Pricing**: You only pay for actual results delivered (`app-analyzed`), never for wasted idle time.
3. **100% MCP & AI Agent Ready**: Designed with strict JSON schemas so **Claude Desktop, Cursor, OpenAI Agents, LangChain, n8n, Make.com, Zapier, and Clay** can call this Actor autonomously as a tool.
4. **Clean, Structured Export**: Download results in **JSON, CSV, Excel, XML, or RSS** directly from the Apify dataset tab or via REST API.

***

### 🎯 Target Industries & High-ROI Use Cases

- **Mobile App Studios & Indie Developers**
- **App Store Optimization (ASO) & UA Agencies**
- **Mobile SDK & Ad-Tech Sales Teams**
- **Product Managers Analyzing Competitor Weaknesses**

#### Popular Workflows

- **Automated B2B Prospecting & Enrichment**: Feed a list of targets from Clay, HubSpot, Salesforce, or Google Sheets and enrich every record automatically.
- **Autonomous AI Agent Tooling (MCP)**: Connect this Actor to Claude or Cursor via the official `@apify/actors-mcp-server` so your AI assistant can gather real-time live web intelligence on demand.
- **Scheduled Monitoring**: Schedule recurring runs (daily/weekly) on Apify and push fresh intelligence to Slack, Webhook, Email, or PostgreSQL/Supabase.

***

### 📥 Input Parameters Example

```json
{
  "searchTerms": [
    "habit tracker",
    "invoice maker"
  ],
  "maxAppsPerTerm": 5
}
```

***

### 📤 Output Dataset Sample

Each extracted record is pushed to the default Apify Dataset in clean JSON format:

```json
{
  "trackName": "Habit Tracker Pro",
  "sellerName": "Productive Labs LLC",
  "averageUserRating": 4.75,
  "userRatingCount": 18420,
  "lowStarComplaintsCount": 6,
  "complaintThemes": [
    "subscription-paywall-pricing",
    "login-sync-account"
  ],
  "sellerUrl": "https://productivelabs.example.com"
}
```

***

### 🤖 How to Use with AI Agents (Claude / Cursor MCP) & Automation

#### 1. Model Context Protocol (MCP)

Add this Actor to your `claude_desktop_config.json` or Cursor MCP settings using the Apify MCP Server:

```json
{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--actors",
        "lucacelli/apple-appstore-aso-competitor-review-intel"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_API_TOKEN"
      }
    }
  }
}
```

#### 2. Call via Apify REST API (cURL / n8n / Make / Clay)

```bash
curl -X POST "https://api.apify.com/v2/acts/lucacelli~apple-appstore-aso-competitor-review-intel/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms":["habit tracker","invoice maker"],"maxAppsPerTerm":5}'
```

#### 3. Node.js SDK

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

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('lucacelli/apple-appstore-aso-competitor-review-intel').call({"searchTerms":["habit tracker","invoice maker"],"maxAppsPerTerm":5});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

***

### ❓ Frequently Asked Questions (FAQ)

#### Does this Actor require paid external API keys or residential proxies?

No! It works out of the box with zero external API keys required.

#### Can I export the data to CSV, Excel, or Google Sheets?

Yes. Every run stores structured records in an Apify Dataset that can be exported with one click to **CSV, XLSX (Excel), JSON, or JSONL**, or synced directly to Google Sheets via Make/Zapier/n8n.

#### Keywords & Tags

`Apple App Store scraper, iOS review scraper, ASO competitor research, mobile app lead generation, user complaint analysis, SensorTower alternative`

# Actor input Schema

## `searchTerms` (type: `array`):

Keywords or app names to analyze on the iOS App Store (e.g. \["habit tracker", "invoice maker"]).

## `maxAppsPerTerm` (type: `integer`):

Number of top apps to analyze per search keyword (1 to 25).

## Actor input object example

```json
{
  "searchTerms": [
    "habit tracker"
  ],
  "maxAppsPerTerm": 5
}
```

# Actor output Schema

## `dataset` (type: `string`):

Link to the extracted JSON items stored 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 = {
    "searchTerms": [
        "habit tracker"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("lucacelli/apple-appstore-aso-competitor-review-intel").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 = { "searchTerms": ["habit tracker"] }

# Run the Actor and wait for it to finish
run = client.actor("lucacelli/apple-appstore-aso-competitor-review-intel").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 '{
  "searchTerms": [
    "habit tracker"
  ]
}' |
apify call lucacelli/apple-appstore-aso-competitor-review-intel --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,lucacelli/apple-appstore-aso-competitor-review-intel"
        }
    }
}
```

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/OZbwOARsIb7GLDfn7/builds/0IDtwUYb2IMy621N6/openapi.json