The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Storelift listing page.
App Store & Google Play ranking data as an MCP server. Ask your assistant which keywords you dropped on, who outranks you, and whether AI assistants mention your app at all — without opening a dashboard.
Backed by Storelift, which measures keyword ranks, rivals and AI visibility nightly. No store credentials required — ranks are read from the public storefront.
Claude Code:
Claude Desktop (claude_desktop_config.json):
| Tool | Returns |
|---|---|
list_apps | tracked apps (id, name, countries, keywords) |
get_keywords | keyword ranks for one app in one country |
get_rivals | apps ranking above you, with their rank and yours |
get_ai_visibility | whether assistants name your app, per engine (Claude / ChatGPT / Gemini) |
get_history | rank history, [day, rank] points |
Keyword results carry three distinct states, and they are not the same thing:
measured: false — the query could not be measuredrank: null (with measured: true) — measured, but absent from the top resultsrank: <number> — the rankCounting an unmeasured day as zero produces a false chart. The tool descriptions repeat this so the model does not flatten the three into one.
AI visibility is an observation, not a ranking: a model's knowledge is frozen at a date and the answer is not identical every time. Read the trend, not a single measurement.
| Variable | Default | Purpose |
|---|---|---|
STORELIFT_API_KEY | — | required; the server exits with a message if unset |
STORELIFT_API | https://storelift.net | override the API base |
No dependencies — a single file speaking JSON-RPC over stdio. Runs with
node index.mjs just as well as through npx.
Published to the official MCP Registry as net.storelift/storelift.
The manifest is server.json in this repo.
github.com/vom-core/storelift-mcp — one file, no dependencies. Read it before you run it.
MIT