The AIM App Store is the directory for the AI stack — AI agents, MCP servers, coding tools, and vibe-coded apps, indexed and organised by community Spaces.
The practical obstacle to adopting AI tooling is rarely capability; it is discovery. New agents, MCP servers, and developer tools appear faster than any team can track, and the ones that matter for a specific job are buried among the ones that do not.
Generic search does not solve this. It surfaces what is popular or well-marketed, not what fits a particular stack, and it has no notion of whether two tools are alternatives or complements.
The cost of poor discovery is not just wasted search time. It is duplicated build effort — teams writing integrations that already exist — and it is adoption of whichever tool was easiest to find rather than whichever fits.
The directory indexes AI agents, MCP servers, coding tools, and vibe-coded apps as distinct categories, so a team looking for a protocol server is not sifting through consumer apps.
Community Spaces add the organising layer a flat index cannot: groupings shaped by the people using the tools, which is usually a better guide to what belongs together than a taxonomy imposed up front.
An index of a fast-moving ecosystem decays quickly. Keeping listings current is the ongoing work, and it is the reason a directory is a product to operate rather than a page to publish.
Agents, MCP servers, coding tools, and finished apps sit at different layers of a working setup, and they are chosen at different times by different people. Separating them means a search returns candidates that are genuinely substitutable for one another.
That distinction matters more in this ecosystem than in most, because the same label is applied to a protocol server and a consumer chat app.
The AIM App Store is live, indexing AI agents, MCP servers, coding tools, and apps across community Spaces.
Listing counts and traffic figures are not published, so none are stated here.
Build-versus-buy is the decision that most determines the cost of an AI programme, and it cannot be made well without a current view of what exists. Teams that skip the survey step reliably rebuild something that was available, or adopt something that does not fit.
The practical habit is to timebox a survey before each build decision, record why the existing options were rejected, and revisit that record when the ecosystem moves — which, in this domain, is roughly every quarter.
Related service: AI Transformation Consulting. See the rest of the work on the case studies index and the portfolio.
Bring one workflow, its owner, and what an incorrect result would cost. That is enough to scope the first engagement.