appstore-intel-mcp
Allows searching apps, fetching metadata, and retrieving reviews from the Apple App Store.
Allows searching apps, fetching metadata, and retrieving reviews from the Google Play Store.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@appstore-intel-mcpsearch for fitness tracking apps on Apple App Store"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
title: Appstore Intel MCP emoji: š± colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false
appstore-intel-mcp
A remote MCP server that gives AI agents structured access to Google Play and the Apple App Store ā search apps, fetch metadata, pull reviews. OAuth-protected, Streamable HTTP, deployable in minutes.
Status: early. v0.1 ships three tools. v0.2 adds review analysis, competitor discovery, and release-tracking webhooks.
Related MCP server: App Store Connect MCP Server
Tools
Tool | Purpose |
| Free-text search across a storefront, returns identifiers |
| Full listing for one app (title, version, rating, screenshots, ā¦) |
| Paginated reviews, sortable by recency / rating / helpfulness |
Quickstart (local)
git clone https://github.com/gautam84/appstore-intel-mcp
cd appstore-intel-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env # leave API_KEYS empty for dev
python -m appstore_intel_mcp # serves on http://localhost:8000/mcpConnect from Claude Code:
claude mcp add --transport http appstore-intel http://localhost:8000/mcpDeploy to Koyeb
koyeb secret create api_keys --value "$(openssl rand -hex 32)"
koyeb app create -f koyeb.yamlThen in Claude.ai ā Settings ā Connectors ā Add custom connector, paste https://<your-app>.koyeb.app/mcp and the bearer token.
Architecture
mcp client āāāŗ Streamable HTTP āāāŗ FastMCP
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providers/google_play providers/app_store
(google-play-scraper) (iTunes Search + RSS)All tool calls go through an in-memory TTL cache (cache.py) so the same app metadata isn't re-scraped every request.
Roadmap
analyze_reviewsā theme extraction + sentiment using a local embedding modelcompare_appsā side-by-side matrixfind_competitorsā category + embedding-based similarityRelease/rating-drop webhooks
Full OAuth 2.1 + PKCE flow for multi-tenant hosting
Redis cache for horizontal scale
License
MIT.
This server cannot be deployed
Maintenance
Related MCP Connectors
Your agent needs app-store data ā what an app looks like on the App Store and Google Play, what reviewers say, and what ranks for a search in a given country. **What you can ask for** ⢠"What does this app's store listing look like, and how is it rated?" ⢠"Pull recent reviews for this app and group the complaints." ⢠"What apps rank for this search term in Japan?" ⢠"List the top apps in this category on both stores." ⢠"Compare this app's listing on iOS and Android." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-apps/mcp and sign in with OAuth ā there is no key to create or paste. 37 tools: Apple App Store and Google Play app info, app lists, category listings, reviews and store search results, in live and queued forms, with categories, languages and locations for each. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the store listing here, then ask the same agent what the app's website ranks for ā without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo/mcp for all of it at once ā rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
Live App Store & Google Play data for AI agents: app discovery, ASO keywords, reviews.
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
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