Connect AI to App Store Data
AppTail’s MCP server lets AI assistants read your real App Store data — Apple’s own measured downloads and proceeds, keyword rankings, competitors, reviews and market charts — and act on your account.
Model Context Protocol (MCP) is an open standard that creates universal connections between AI applications and external data sources. Connect once — use everywhere.
ASO Data Your AI Can Read and Act On
Search apps, track keyword rankings, monitor competitors, and pull market data — all through natural language prompts.
What happened
explain_period
“What happened last month?” in one call — downloads and proceeds against the month before, the storefronts that moved, the keywords that fell and rose, every detected finding, and review volume
get_signals
Rank moves, chart entries, review spikes, competitor releases and price changes — each with the two numbers behind it and the sentence that states the finding
get_account
Your apps, your plan and its limits, App Store Connect health per app, and the storefronts you focus on — the call an assistant should make first
Downloads & revenue requires App Store Connect
get_performance
Apple’s own measured impressions, product page views, downloads, sales and proceeds — for one app or the whole portfolio, split by storefront or by Apple’s own attribution
These are the only download and revenue figures AppTail reports as fact, and they exist only for apps you have connected. Figures for anybody else’s app are a third-party model, and every tool that returns one says so.
Keyword research & rank tracking
get_keywords
Every question about tracked terms: where each ranked at the start and end of a window, how far it moved, its day-by-day history, the same terms measured for named competitors, and the daily top-1/3/10/30/50/100 counts. Sorted worst movement first
get_keyword_serp
Who ranked for a term on a day you name, with names, subtitles and ratings — the “and why” half of a drop
discover_keywords
Terms you could be tracking and are not, from four sources at once — what your rivals rank for, what the store’s similar apps rank for, what the store’s own mining surfaced, and what your listing itself suggests when a model reads it cold
add_keywords
/
remove_keywords
/
tag_keywords
Change what you track, in batches, with an outcome per item. Tagging is the one worth handing over: sorting four hundred terms into brand, generic and competitor is an afternoon by hand and one call here
Competitor analysis
get_competitors
The rivals tracked against one of your apps, and how many more your plan allows
add_competitors
/
remove_competitors
Track rivals by name, App Store URL or id — “add Fuelio and Drivvo” is one call, and the answer says what happened to each of them
Any app in the store
search_apps
Find any App Store app by name in any language, bundle ID, or store URL — and import one we have never seen
get_app
One app at the depth you ask for: the listing, and optionally its rating in every storefront and over time, the charts it ranks in, its organic reach, the publisher’s portfolio, release history and screenshots
get_reviews
Reviews over any window, filtered by storefront, star rating, text or whether the developer replied — with volume and rating trends beside them. Hand your assistant three hundred filtered reviews and it will find the themes itself
get_top_charts
Top free, paid and grossing charts by storefront and category, marked with which entries are already yours or already tracked
add_app
Claim an app as one of your own. Crawling starts at once, so rankings, reviews and competitors follow within a day
Saved workflows
monthly_review
A full month in review for one app or the whole portfolio: the money, the movers, the findings, and what is worth doing next.
weekly_standup
The week in five lines: what moved, what needs answering, and nothing I have already been emailed.
keyword_triage
Go through one app's tracked terms: what dropped and why, what is worth adding, and tag the corpus into brand, generic and competitor.
competitor_brief
What a rival has been doing: where they rank against you, what shipped, what their users are saying, and where they are beating you.
answer_reviews
Read one app's unanswered reviews, draft a reply to each in the reviewer's own language, and — after you have approved them — publish them to the App Store.
Picked out of your assistant’s own prompt menu rather than typed. Each one is a sequence over the tools above, written out so the answer comes back in the same shape every time.
Connect in minutes
Add the AppTail MCP server to your AI assistant with a single configuration block. OAuth handles authentication automatically.
Create a free AppTail account
Sign up to get access to the MCP server and start tracking apps.
Add the server config to your AI client
Copy the configuration below into your AI assistant's MCP settings.
Authorize and start using
You'll be prompted to authorize with your AppTail account when first connecting. After that, your AI assistant has full access to ASO tools.
Add to your claude_desktop_config.json:
{
"mcpServers": {
"apptail-aso": {
"url": "https://apptail.io/mcp/aso"
}
}
}What can you do with ASO data in AI?
Here are example prompts you can use with your AI assistant once connected to AppTail.
"Analyze the top 10 finance apps in the US App Store and compare their keyword strategies"
"Find keyword suggestions for my app and show which ones have the least competition"
"Compare my app's keyword positions against my top 3 competitors over the last month"
"Show me recent 1-star reviews for my app and summarize the most common complaints"
Grow your apps with data-driven insights
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