Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Free App Store competitive intelligence for Claude — rival downloads, revenue, and ASO keywords.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
ASO_CACHE_TTL_HOURS必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"app-store-operator": {
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add app-store-operator --env ASO_CACHE_TTL_HOURS=your-value -- npx -y app-store-operator動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"app-store-operator": {
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"app-store-operator": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
ASO_CACHE_TTL_HOURS envこの設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月8日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント meyusufdemirci で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.3.5)がレジストリに届いたのは3 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, VMware Storage
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:↓ 週あたりのダウンロードi★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
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作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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
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.
research_rivalsFinds 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:
Cached for 24 hours, so asking again about the same keyword and country costs nothing and opens no browser.
search_app_storeSearches 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_detailsFetches 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:
Never cached — every call scrapes fresh, at roughly 10–20 seconds per app ID.
prepare_iaeGenerates 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.
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.
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.
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.npx playwright install chromium yourself.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"]
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 StoreSearch the App Store for "beard style" in FrancePrepare an in-app event for a summer hairstyle campaign in en-gbNo 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.
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 |
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.
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.
research_rivals is fixed at the top 3 resul冒頭だけを表示しています。続きはリポジトリで読めます