App Store Operator

<p align="center"> <a href="https://www.npmjs.com/package/app-store-operator"><img src="https://img.shields.io/npm/v/app-store-operator?color=6366f1&label=npm" alt="npm"></a> <a href="https://www.npmjs.com/package/app-store-operator"><img src="https://img.shields.io/npm/dm/app-store-operator?color=818cf8&label=downloads" alt="downloads"></a> <a href="#license"><img src="https://img.shields.io/badge/license-MIT-green" alt="license"></a> <a href="https://nodejs.org"><img src="https://img.shields.io/badge/node-%E2%89%A518-brightgreen" alt="node"></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-compatible-6366f1" alt="MCP"></a> <a href="https://lobehub.com/mcp/meyusufdemirci-app-store-operator"><img src="https://lobehub.com/badge/mcp/meyusufdemirci-app-store-operator" alt="MCP Badge"></a> </p>

App Store competitive intelligence, inside Claude.

App Store Operator is an MCP server that brings App Store research directly into your AI assistant. Instead of switching to a dashboard, you ask Claude for ranked keyword results, competitor download and revenue estimates, or App Store Connect-ready In-App Event copy — and get the answer in the same conversation where you are making the decision.

Built for indie iOS developers who want research inside their workflow rather than in another browser tab. Free and open source (MIT). A lightweight alternative to SensorTower, AppTweak, and AppFollow for iOS-only competitive research.

claude mcp add --transport stdio app-store-operator -- npx -y app-store-operator@latest

→ app-store-operator.com · Setup guide

What it does

Searches the App Store for competing apps on a given keyword and pulls detailed analytics from SensorTower — downloads, revenue, ratings, top markets, publisher info, and more.

search_app_store and prepare_iae work with no account at all. research_rivals and get_app_details open a browser once for a free SensorTower sign-in, then reuse that saved session — no paid plan, no API key.

Everything the server exposes — four tools, six prompts, seven resources — is read-only. Nothing writes to your App Store Connect account, to SensorTower, or anywhere but a local cache file.

Tools

research_rivals

Finds the top 3 apps for a keyword and returns a full metrics report for each.

| Parameter | Type | Description | |-----------|------|-------------| | keyword | string | Search term to look up (e.g. meditation, psikoloji) | | country | string | Two-letter country code (e.g. us, tr, gb) |

Returns for each competitor:

• App Store & SensorTower URLs
• Worldwide and last-month downloads & revenue
• Rating score and rating count
• Publisher, categories, top markets
• Release date, last updated, supported languages
• In-app purchases and ad network presence

Cached for 24 hours, so asking again about the same keyword and country costs nothing and opens no browser.

---

search_app_store

Searches the App Store for a keyword and returns ranked results as a markdown table — instantly, no SensorTower required.

| Parameter | Type | Description | |-----------|------|-------------| | keyword | string | Search term to look up | | country | string | Two-letter country code | | limit | number | Number of results to return (1–25, default 3) |

Use this to discover which apps rank before deciding which to analyse. Follow up with get_app_details for analytics on specific apps.

---

get_app_details

Fetches SensorTower analytics for one or more app IDs you already have.

| Parameter | Type | Description | |-----------|------|-------------| | app_ids | array | Numeric App Store IDs (e.g. from search_app_store) | | country | string | Two-letter country code |

Returns for each app:

• Downloads and revenue (worldwide + last month)
• Rating score and rating count
• Publisher, categories, top markets
• Release date, last updated, supported languages
• In-app purchases and ad network presence

Never cached — every call scrapes fresh, at roughly 10–20 seconds per app ID.

---

prepare_iae

Generates iOS App Store In-App Event (IAE) copy — 3 variations in the target language, then a final report.

| Parameter | Type | Description | |-----------|------|-------------| | keywords | array | Ordered keywords by priority (index 0–2 = Tier 1, 3–6 = Tier 2, 7–9 = Tier 3) | | locale | string | Target locale (e.g. en-us, en-gb, de-de, tr, ja, ko) | | event_purpose | string | What the event is about and why users should care | | audience | string | Target audience (e.g. students, professionals, parents) | | event_context | string | Real-world hook tying the event to a moment (e.g. a holiday, season) | | goal | string | Primary conversion goal (e.g. attract new users, boost engagement) | | tone | string | Copy tone: Engaging, Playful, Motivational, Authoritative, Calm, or Urgent |

Returns: a structured brief used to generate 3 copy variations, each with event name (≤30 chars), short description (≤50 chars), and long description (≤120 chars).

---

Any field SensorTower does not expose, or keeps behind its paywall, comes back as N/A. The server reports the gap rather than filling it, and the prompts below tell the assistant to do the same.

Prompts

Six ready-made workflows that already chain the tools above, so you don't have to describe the sequence yourself. In Claude Code they appear as slash commands; other clients surface them in a prompt picker.

| Prompt | Arguments | What it does | |--------|-----------|--------------| | competitor_snapshot | keyword, country | Pulls rival analytics for a keyword, then reads out who owns it and how contested it is | | keyword_shortlist | seed_keyword, country, count? | Expands a seed keyword into candidates, tests each against live search results, and ranks them attack / watch / skip | | app_teardown | app_ids, country | Teardown of known apps — scale, standing, monetisation, reach, momentum, acquisition | | positioning_gap | keyword, country, my_app_id | Puts your app on the same measuring stick as the incumbents and separates behind from attackable | | metadata_rewrite | app_name, keyword, country, must_keep? | Three name / subtitle / keyword-field variations, character-counted against Apple's limits | | in_app_event | event_context, locale, keywords?, audience?, tone? | Runs the full In-App Event flow, asking for whatever prepare_iae still needs |

Arguments marked ? are optional. Every prompt tells the assistant not to invent figures and, where SensorTower is involved, not to quietly fall back to a weaker tool when login is required.

Resources

Reference data and local state a client can attach as context without spending a tool call on it.

| URI | Type | Contents | |-----|------|----------| | asops://guide/tool-selection | markdown | Which tool to use, what each costs, how the SensorTower login works | | asops://reference/country-codes | markdown | Two-letter storefront codes by region | | asops://reference/aso-fields | JSON | App Store Connect character limits and which fields are indexed for search | | asops://reference/iae-fields | JSON | In-App Event limits, artwork sizes, copy rules, keyword tiers | | asops://reference/iae-locales | JSON | Every locale prepare_iae accepts — generated from the same table the tool validates against | | asops://cache/research | JSON | What has already been researched on this machine, and whether it is still fresh | | asops://cache/research/{country}/{keyword} | JSON | One cached research_rivals result, without re-scraping |

Nothing here leaves your machine: the reference resources are static, and the two cache resources read ~/.app-store-operator/cache.json.

Requirements

• Node.js v18+
• A desktop session for research_rivals and get_app_details. They drive a real, visible Chromium window so you can sign in to SensorTower, so they need a display — they do not work over plain SSH or inside a container. The other two tools have no such requirement.
• Disk space for Chromium. Installing the package downloads a Playwright Chromium build (a few hundred MB) via a postinstall step. If that step fails, the server installs it on first use instead; you can also run npx playwright install chromium yourself.

Usage

As an MCP server (Claude Code / Claude Desktop / OpenAI Codex)

Claude Code — run this command once:

claude mcp add --transport stdio app-store-operator -- npx -y app-store-operator@latest

Claude Desktop — add to your MCP config:

{
  "mcpServers": {
    "app-store-operator": {
      "command": "npx",
      "args": ["app-store-operator@latest"]
    }
  }
}

OpenAI Codex — run this command once:

codex mcp add app-store-operator -- npx -y app-store-operator@latest

Codex stores MCP servers in ~/.codex/config.toml. If you prefer to edit it directly:

[mcp_servers.app-store-operator]
command = "npx"
args = ["-y", "app-store-operator@latest"]

# Optional but useful for SensorTower scraping flows
startup_timeout_sec = 20
tool_timeout_sec = 180

Then restart Codex or start a new thread, and ask things like:

• Research rivals for "hairstyle" in the GB App Store
• Search the App Store for "beard style" in France
• Prepare an in-app event for a summer hairstyle campaign in en-gb

No installation step needed — npx fetches and runs the package automatically.

The server communicates over stdio and is designed to be invoked by an MCP client. It advertises server-wide instructions during initialize so clients route between the tools correctly, and returns an MCP tool error when SensorTower login is required.

Configuration

Both settings are optional environment variables on the server process.

| Variable | Default | What it does | |----------|---------|--------------| | ASO_CACHE_TTL_HOURS | 24 | How long a research_rivals result stays fresh in the local cache before it is scraped again | | ASO_DEBUG_RATINGS | unset | Set to 1 to print SensorTower's ratings panel to stderr when a rating score or count comes back N/A — useful when reporting a scraping bug |

In a container

The repo ships a Dockerfile built on Playwright's official image:

docker build -t app-store-operator .
docker run -i --rm app-store-operator

The server speaks JSON-RPC over stdio, so no port is exposed — point your MCP client at the container's stdin/stdout. Note that a container has no display: search_app_store and prepare_iae work there, but the two SensorTower tools cannot open a login window and will fail rather than prompting you to sign in.

How it works

1. Searches the App Store for the keyword and country, and looks up any app IDs you passed directly against Apple's public iTunes Lookup API
2. For each app, drives a Chromium browser to scrape SensorTower analytics
3. Extracts the metrics and returns a compiled report

SensorTower data is scraped via Playwright because it is rendered client-side.

A browser window will open. This is deliberate, not a bug: SensorTower requires a login, so the first run opens a visible window for you to sign in. The session is saved to ~/.app-store-operator/profile and reused on every later call, so you only log in once. If a tool reports not_logged_in, finish signing in on that window and run the tool again.

Results from research_rivals are cached for 24 hours in ~/.app-store-operator/cache.json — override the TTL with the ASO_CACHE_TTL_HOURS environment variable.

Limitations

• iOS only. Nothing here covers Google Play or Android.
• Read-only. No tool changes anything in App Store Connect or on SensorTower.
• **research_rivals is fixed at the top 3 resul