appstore-intel-mcp
A remote MCP server that gives AI agents structured access to Google Play and the Apple App Store — search apps, fetch metadata, pull reviews.
README
title: Appstore Intel MCP emoji: 📱 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 — 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.
Tools
| Tool | Purpose |
|---|---|
search_apps |
Free-text search across a storefront, returns identifiers |
get_app_metadata |
Full listing for one app (title, version, rating, screenshots, …) |
get_reviews |
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/mcp
Connect from Claude Code:
claude mcp add --transport http appstore-intel http://localhost:8000/mcp
Deploy to Koyeb
koyeb secret create api_keys --value "$(openssl rand -hex 32)"
koyeb app create -f koyeb.yaml
Then in Claude.ai → Settings → Connectors → Add custom connector, paste https://<your-app>.koyeb.app/mcp and the bearer token.
Architecture
mcp client ──► Streamable HTTP ──► FastMCP
│
┌──────────────────┼──────────────────┐
▼ ▼ ▼
tools/search tools/metadata tools/reviews
│ │ │
└──────► registry ─┴──────────────────┘
│
┌─────────────┴─────────────┐
▼ ▼
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_reviews— theme extraction + sentiment using a local embedding model - [ ]
compare_apps— side-by-side matrix - [ ]
find_competitors— category + embedding-based similarity - [ ] Release/rating-drop webhooks
- [ ] Full OAuth 2.1 + PKCE flow for multi-tenant hosting
- [ ] Redis cache for horizontal scale
License
MIT.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.