App Store Operator

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.

Related MCP server: LaunchFast MCP

Tools

research_rivals

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

Returns for each competitor:

• App Store & SensorTower URLs

• Worldwide and last-month downloads & revenue

• Rating score

• Publisher, categories, top markets

• Release date, last updated, supported languages

• In-app purchases and ad network presence

search_app_store

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

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.

Returns for each app:

• Downloads and revenue (worldwide + last month)

• Rating score

• Publisher, categories, top markets

• Release date, last updated, supported languages

• In-app purchases and ad network presence

prepare_iae

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

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).

Requirements

• Node.js v18+

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.

How it works

1. Queries the iTunes Search API for the top 3 apps matching the keyword and country

2. For each app, drives a Chromium browser to scrape SensorTower analytics

3. Extracts metrics and returns a compiled plain-text 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.

Project structure

src/
├── index.js                    # MCP server setup and request handlers
├── shared.js                   # App Store lookup + SensorTower scraping
├── cache.js                    # 24h local cache (research_rivals only)
└── tools/
    ├── research-rivals.js      # research_rivals tool
    ├── search-app-store.js     # search_app_store tool
    ├── get-app-details.js      # get_app_details tool
    └── prepare-iae.js          # prepare_iae tool
scripts/postinstall.js          # installs Playwright Chromium on install
test/smoke-test-mcp.js          # stdio smoke test

Development

Run the smoke test to verify the server boots and exposes every tool over stdio — it checks initialize (including server instructions) and tools/list:

npm run smoke