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gautam84

appstore-intel-mcp

by gautam84
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Python
Hybrid

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.

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

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