How ChatGPT Chooses Apps and Tools to Show
Most discussions about ChatGPT Apps focus on how to build them or where to find them. Far fewer explain what actually happens behind the scenes when ChatGPT decides to surface one during a conversation.
For developers and businesses investing in the format, this question matters more than it might seem. Understanding how the system selects apps and what signals drive that selection changes how you design your app, how you write its tool descriptions, and how you think about visibility over time.
This article explains the mechanics as clearly as current public information allows.
The three ways apps enter a conversation
Before getting into what influences selection, it helps to be clear on the three distinct ways an app can appear inside ChatGPT.
The first is direct mention. A user types the app's name at the start of a message. For example, "@Spotify, make a playlist for my Friday dinner party" and ChatGPT surfaces that specific app.
The second is tools menu selection. A user opens the tools panel, sees connected apps, and manually selects one before or during a conversation. Again, the user is making an active decision.
The third and the one worth understanding deeply is indirect prompting. ChatGPT reads the conversation and proactively suggests a relevant app, without the user asking for it. Someone planning a trip to Barcelona finds a Booking.com widget appear mid-conversation. Someone drafting a presentation gets a Canva suggestion before they've asked for one.
This third mechanism is what OpenAI calls the "indirect prompting" system, and it's the one where the underlying model is actually making a selection decision.
What drives indirect prompting
Indirect prompting
The most immediate signal is the content of the current conversation. The model reads what the user is saying, identifies the intent, and asks whether any connected app can meaningfully help with that intent right now.
This is not keyword matching. The model is evaluating the full conversational context, what the user is trying to do, what they've already established, what stage of the task they're at.
App tool definitions
This is the part developers have direct control over, and it matters more than most realize.
Every ChatGPT App is built on MCP tools discrete capabilities that the model can invoke. Each tool has a name and a description. Those descriptions are, in effect, the model's instruction manual for when and how to use the app.
OpenAI's submission guidelines are clear: tool descriptions must explain what the tool does, in plain language, without promotional language, without comparisons to other apps, and without instructions that encourage the model to select the tool beyond its actual purpose. A tool called search_apartments with a description that reads "searches available apartments by location, price range, and move-in date" gives the model precise criteria for when to reach for that tool. A vague or promotional description makes it harder for the model to make that call accurately.
OpenAI has publicly stated that incorrect or missing tool annotations are a common cause of app rejection. The same precision that determines whether an app passes review also determines how reliably the model can match it to relevant conversations.
Practically, this means that app visibility in contextual surfacing is partly a writing problem. The clearer and more accurate your tool definitions, the more confidently the model can suggest your app when a user's intent matches.
Quality and user satisfaction
OpenAI's submission guidelines state directly that apps demonstrating strong real-world utility and high user satisfaction may be eligible for enhanced distribution opportunities, including directory placement and proactive suggestions.
This language is worth reading carefully. Featured placement and proactive recommendations are explicitly described as outcomes of quality and satisfaction, not of payment or submission timing. OpenAI has built explicit feedback mechanisms into the app experience, and user responses to those mechanisms feed back into how often an app gets surfaced.
The practical implication for businesses
Taken together, the selection system rewards precision over scope. Apps that try to do everything, with broadly described tools and a wide surface area, are harder for the model to match to specific user intents. Apps that do a few things very well, with clearly defined tools and a tight scope, are easier for the model to reach for at the right moment.
The same dynamic applies to visibility over time. Indirect prompting is not a static ranking. It is an ongoing signal, a system that learns which apps earn their recommendations by completing tasks and which ones don't. Apps that deliver on the first interaction tend to be recommended again. Apps that don't tend to be surfaced less often.
The platform surfaces apps it trusts to help. The way to earn that trust is to define the help precisely, and then actually deliver it.