OpenAI GPT Store Ideas + How to Connect an API to Your GPTs

OpenAI’s upcoming GPT Store is expected to be flooded with easy-to-build custom GPTs, so discoverability and real-world usefulness will likely decide which ones earn money. With a low barrier to entry, creators face a crowded market where copying a winning idea is straightforward; that makes search, SEO-like signals, leaderboards, and any form of paid placement (if it exists) central to getting users to notice a GPT. Monetization also looks uncertain: whether creators get paid through user subscriptions (like ChatGPT Plus) or through direct purchases, the practical takeaway is that only GPTs that deliver clear value—especially ones that solve a hard problem efficiently—are likely to stand out.

To illustrate what “useful” can look like, the transcript walks through three example GPT concepts. The first is an “Encode/Decode” pair that encrypts a user’s message and then instructs the user to decode it using a second GPT. The workflow takes a plaintext message, runs Python encryption using Fernet to produce an encrypted payload plus a secret key, and then provides decoding instructions that route the user to the decode GPT. The decode GPT asks for the encoded message and the secret key, reruns the Fernet logic, and prints the original message. It works as a demonstration of combining two GPTs and using Python code, but it’s framed as unlikely to be a serious product because it’s too narrow and easy to replicate.

The second example focuses on connecting a GPT to a third-party model via an API. A GPT called “Mistral GPT” is configured with an action that calls the Mistral API—specifically using the “mistral tiny” model. When a user asks a question (the transcript uses “what are the best reasons to use an open source llm”), the GPT triggers the action, sends the query to the API, and returns the response inside the GPT conversation. The point is less about the specific question and more about the mechanism: GPTs can act as front-ends to external services, including open-source LLMs, as long as the GPT is set up with the right action and API connectivity.

The third section shows how to build those API actions using an OpenAPI schema. When creating a new GPT, the creator can add an “action” by providing an OpenAPI schema. The transcript describes using a helper GPT (“Open AI schema”) to convert third-party API documentation into a usable OpenAPI schema. After pasting the documentation for an example “cat API,” the schema is generated and inserted into the GPT’s action configuration. The GPT then requests an API key (using a Bearer token), saves it, and can call the API to fetch and display images based on user prompts (e.g., “a Main cat”). The transcript notes that actions can become complex when many are combined, but they unlock powerful capabilities—especially when paired with other tools like code interpreter.

Overall, the transcript’s core message is pragmatic: the GPT Store will reward GPTs that make difficult tasks easy through real integrations—APIs, specialized knowledge, or non-trivial code—while generic, superficial ideas will struggle in a crowded marketplace.