How do ChatGPT apps get discovered?
Discovery is the first problem any app has to solve. It doesn't matter how well your ChatGPT App works if no one knows it exists. But discovery inside ChatGPT works differently from how it works in traditional app stores and that difference matters for how you build, describe, and position your app.
There are three distinct ways a user can encounter a ChatGPT App. Each one has a different trigger, a different audience, and different things a developer can do to influence it.
The ChatGPT App Directory
The most direct path is the App Directory, accessible at chatgpt.com/apps or through the tools menu inside any ChatGPT conversation. Users can browse by category - Featured, Lifestyle, Productivity, and others or search by name or use case.
For discovery through the directory, the basics are familiar. Your app needs a clear name, an accurate description, and a defined set of use cases. The important nuance is how users search. Most people don't open the directory knowing what app they want. Your app's description needs to map to that language, task-oriented and specific, not product-oriented and vague.
Apps that meet a higher standard of design and functionality may be Featured by OpenAI. Featured placement means more prominent positioning in the directory and, in some cases, more frequent surfacing in conversations. According to OpenAI's published submission guidelines, featured status reflects both quality of the app experience and clarity of the value it delivers.
Direct Mention
Once a user has connected your app, they can call it by name in any future conversation using an @ mention. For example"@Booking.com, find me a hotel in Amsterdam for next Friday." The app activates immediately within the chat, and ChatGPT passes the conversational context so the app knows what the user is working on.
This mechanism is powerful for retention, but it only works with users who already know your app. It doesn't solve the cold-start discovery problem. For that, direct links matter: a deep link from your website, an email, or a social post that takes users directly to your app's directory page reduces the friction between hearing about your app and connecting it.
The naming implication here is practical. An app name that's memorable, specific, and distinct makes direct mention more reliable. If users can't recall the exact name, they won't invoke it.
Indirect prompting
This is the most interesting mechanism, and the one most specific to how AI platforms work.
ChatGPT watches the conversation for intent signals and proactively suggests relevant apps before the user asks for them. Someone planning a move to a new city might see Zillow appear in the conversation when real estate comes up. Someone working through a presentation might see Canva. The trigger is not a search, it's the model recognizing that a user is trying to do something your app helps with.
OpenAI describes this as driven by conversational context, historical usage patterns, and user preferences. In practice, that means the model learns which apps get invoked in which situations, and starts anticipating those matches. Apps that consistently complete tasks well get surfaced more often in similar conversations.
A user who has never heard of your app can encounter it mid-conversation, at exactly the moment they have the problem your app solves. No search, no browsing, no awareness required.
It also means that discovery and performance are directly linked. An app that delivers well returns relevant results, completes tasks without errors, interacts naturally in conversation, earns more suggestions over time. An app that produces poor results gets surfaced less.
What This Means for How You Build Your App
The three discovery mechanisms each reward different things.
Directory discovery rewards clear, task-oriented metadata - app descriptions that match how users naturally describe their needs.
Direct mention rewards memorability - an app name users can recall and a product they want to use again.
Indirect prompting reward performance - an app that reliably completes the tasks it promises, so the model learns to recommend it in relevant situations.
None of these are independent. An app with vague metadata won't get selected by the model in contextual situations, because the model can't tell when it's relevant. An app with a great description but a poor experience will stop getting surfaced as feedback signals accumulate. An app that works well but has no external promotion will struggle with cold-start discovery until contextual suggestions kick in.
The developers who will compound early in this ecosystem are the ones who optimize across all three: metadata that maps to user intent, a name users remember, and an experience worth returning to.