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Keyword research eats an afternoon and produces a spreadsheet nobody opens again. Truly a classic. Autocomplete suggestions come from one place, tracked positions from another, and then you check who's ranking above you by typing the term into a phone. By the time the list is finished, half the reason you built it has expired.
The AppFollow MCP server puts that data in...
Six of the app-store tools you already use now have an MCP server, and the pitch barely changes between them: pull your reviews, ratings, and app store optimization data straight into Claude, ChatGPT, or Cursor, no dashboard, no CSV exports.
What that pit...
MCP for app store optimization connects an AI assistant to authorized app-store data—such as reviews, ratings, rankings, and keywords—so an app team can investigate a specific question without exporting the data first.
The time saving appears when a task normally crosses several filters or reports. Instead of collecting two date ranges, separating countries, and pastin...
If you run ASO A/B testing for a mobile game, the next experiment is already sitting in a backlog somewhere. It looks simple on the ticket: new icon, new first screenshot, split the traffic, wait for a winner. Then paid UA shifts your traffic mix mid-run, a LiveOps event lands on day four, and the variant everyone loved finishes 4% ahead with a confidence interval you cannot ...
You can wire a model into your app in an afternoon. Getting it to do useful work without someone watching every step takes months, and most of those months go into everything that is not the model.
A model can read a codebase now. It can search docs, call tools, run the test suite, prepare a change, and respond to whatever came back when it ran. That is a different job...