Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Free App Store competitive intelligence for Claude — rival downloads, revenue, and ASO keywords.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
ASO_CACHE_TTL_HOURSDu brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"app-store-operator": {
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add app-store-operator --env ASO_CACHE_TTL_HOURS=your-value -- npx -y app-store-operatorTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"app-store-operator": {
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"app-store-operator": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"app-store-operator"
],
"env": {
"ASO_CACHE_TTL_HOURS": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
ASO_CACHE_TTL_HOURS envDiese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 8. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto meyusufdemirci bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.5) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Egovframe Scaffold MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:↓ Downloads pro Wochei★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
eGovFrame (전자정부 표준프레임워크) projects: templates, components, CRUD, build, 5.x migration, dependencies
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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 resulNur der Anfang wird gezeigt; den Rest findest du im Repo