Optimizing Content for ChatGPT Plugins and Tools

ClickRadius Institute · Published April 19, 2026

"ChatGPT plugins" is a phrase that means something different now than it did when it first appeared. OpenAI introduced plugins in 2023 as a way for ChatGPT to reach outside its own knowledge and call external services, then folded that capability into a newer, more general design: custom GPTs, Actions, and a built-in browsing tool. The underlying idea survived even as the product name faded. For a business, the practical question is no longer "how do I build a plugin" but "how do I make my content and any interface I expose legible to ChatGPT's tool layer" — the machinery the assistant uses to read the live web and to call external systems on a user's behalf. This article maps that tool layer honestly, separates what is durable from what was a passing product form, and gives you a concrete way to be consumable by it.

What "plugins" became

A short, accurate history matters here because a lot of advice online is optimizing for a product that no longer exists in its original shape.

OpenAI launched the plugin system in 2023. A plugin was, at its core, an external API described by an OpenAPI specification plus a small manifest, which ChatGPT could call to fetch data or take an action. In 2024, OpenAI began winding plugins down and steering developers toward GPTs — customizable versions of ChatGPT — and Actions, which carry the same OpenAPI-described-API idea into that newer container. Separately, ChatGPT gained a browsing tool that retrieves and reads live web pages, and in the developer API the same capability shows up as function calling / tools, where a model is given a set of callable functions and decides when to invoke them.

So the tool layer today has two surfaces a business should care about:

• The reading surface — the browsing tool that fetches and reads your public web content, reaching your site through OpenAI's search and fetch agents.
• The calling surface — GPTs and Actions (and, in the API, function-calling tools) that invoke an API you expose, described by an OpenAPI specification the model reads to decide how to call you.

The plugin brand faded; the mechanism did not. A model that can read a page and call an API is still doing the same two things it did in 2023, just through a cleaner, more general interface.

— ClickRadius Institute, research summary

Why this matters for visibility

No market-wide AI-answer prevalence is established here. Measure the business on a fixed, dated prompt set and record the engines, locale, device or account state where relevant, and whether each result is a mention, linked citation, or recommendation. That sample can identify an observed gap; it does not establish that a category is empty, a citation position is unclaimed, or an early entrant will become a default answer.

It is worth being precise about the two surfaces. A browsing tool may cite a public page it can retrieve and parse; an Action or function may be invoked when the assistant selects that tool for a user's request. Readability and callability create eligibility, not an earned result. Citation and invocation remain separate external outcomes that must be observed for a defined request and time.

Optimizing the reading surface

For a business with ordinary public web content, the reading surface is usually the first surface to inspect. Its requirements overlap with common retrieval inputs, and they are worth restating because they are unglamorous and frequently skipped. Their presence does not establish that a tool read or cited the page.

Be retrievable and allow the crawlers

According to OpenAI's published crawler documentation, its web traffic identifies itself: OAI-SearchBot for surfacing sites in search, ChatGPT-User for user-triggered fetches, and GPTBot for training data. If you want to be eligible for the reading surface, allow the relevant agents in robots.txt, and confirm in your server logs that they are actually reaching you. The browsing tool cannot use a page it could not fetch.

Be extractable and evidence-rich

A page that states its answer plainly and backs it up is far more usable to a reader than one that buries the point. This is the most research-supported lever available. According to Princeton's "GEO: Generative Engine Optimization" study (KDD 2024), three on-page signals affected the selected source's relative visibility in the study's generated responses; statistics, attributed quotations, and source citations — raising visibility by up to roughly 40 percent in the study's benchmarks. Put the direct answer near the top, use question-style headings, keep facts in text rather than images, and support claims with real numbers, named quotes, and links to authoritative sources. The benchmark began with the top five Google sources, randomly selected one source for optimization, and measured that source's relative visibility/impression in generated responses; it did not measure retrieval or citation-inclusion probability, and reported effects varied by domain.

Optimizing the calling surface

If you expose an API — a booking system, a catalog, a lookup, a quote generator — you can make it consumable by a GPT Action or a function-calling tool. The model does not read your marketing; it reads your specification. Treat that specification as documentation written for a reader that reasons.

1. Publish a clean OpenAPI specification. Describe every endpoint, parameter, and response in plain language. The model uses these descriptions to decide when and how to call you, so vague or copy-pasted descriptions produce wrong or skipped calls.
2. Name operations for what they do. Clear operation IDs and summaries ("checkAppointmentAvailability") beat opaque ones ("op_42"). The model is choosing among tools by their described purpose.
3. Give honest examples. Realistic request and response examples help the model form correct calls. Examples that lie about behavior cause failures that get the tool dropped.
4. Use standard authentication and predictable errors. Conventional auth and clear, structured error messages let the model recover gracefully instead of abandoning the interaction.
5. Keep behavior consistent with the spec. A tool that returns something other than what its description promises is unreliable, and an unreliable tool is a tool the model learns to avoid.

Statistics, quotations, and citing sources are among the highest-impact techniques for improving visibility in generative-engine responses.

— Aggarwal et al., "GEO: Generative Engine Optimization," Princeton (KDD 2024)

Reading surface vs calling surface, side by side

The two surfaces reward overlapping virtues through different artifacts. Choosing where to invest depends on whether your value is information or action.

• Artifact you optimize. Reading surface: your public web pages. Calling surface: your OpenAPI specification and the API behind it.
• What the model wants. Reading surface: retrievable, extractable, evidence-rich content. Calling surface: clearly described, reliably behaving endpoints.
• The payoff. Reading surface: a citation (your name in the answer). Calling surface: an invocation (your service used in the workflow).
• Who should prioritize it. Reading surface: nearly every business. Calling surface: businesses whose value is a transaction or lookup a user would want performed.

What does not work, and why

The same anti-patterns fail on both surfaces, for the same underlying reason: the tool layer is mediated by a system that reads meaning and checks consistency, not one that matches strings.

• Keyword-stuffed descriptions in an OpenAPI spec or a page do not fool a reader that understands intent. They just make you less clear, which makes you less usable.
• Misleading manifests or specs — claiming capabilities the API does not have — produce failed calls and get the tool discarded. Reliability is the currency.
• Guaranteed-placement promises. No one can guarantee that ChatGPT will read your page or call your Action; the selection is probabilistic. Anyone offering a guaranteed ChatGPT integration slot is describing something the architecture does not provide.

An honest note on a moving target

This layer changes quickly. Plugins became GPTs and Actions in roughly a year; browsing and function-calling conventions continue to evolve. Anything specific about a product name may age, which is exactly why this article anchors on the durable distinction — reading public content versus calling an API — rather than on a branded feature. Treat per-engine and per-tool behavior as an observed tendency, improve controllable inputs, and then retain defined citation and invocation observations instead of trusting a single result.

Done honestly, this is not a hack; it is clear publishing and interface design. Clean public content and a well-described callable interface can make the two surfaces inspectable and eligible. Only a retained response or invocation establishes what a particular tool actually used.

Frequently asked questions

Are ChatGPT plugins still a thing in 2026?

Not in their original form. OpenAI launched plugins in 2023 and then wound them down in favor of GPTs and Actions, which use the same underlying idea, an OpenAPI-described API the model can call, inside a newer container. Alongside that, ChatGPT gained a built-in browsing tool that reads live web pages. So the practical question is no longer optimizing for plugins as a product but making your content and any API you expose consumable by ChatGPT's tool layer, which the browsing tool reaches through its search and fetch agents.

How do I make my API usable by a ChatGPT GPT or Action?

Publish a clean, accurate OpenAPI specification with clear operation names, plain-language descriptions of every endpoint and parameter, honest examples, and sensible error messages. The model reads those descriptions to decide when and how to call you, so treat them as documentation written for a reader that reasons, not as boilerplate. Keep authentication standard, responses well structured, and behavior consistent with what the descriptions promise, because a tool that behaves unpredictably gets dropped.

Does optimizing for the browsing tool differ from optimizing for API tools?

They share a goal but touch different surfaces. For the browsing tool you optimize your public web content, making pages retrievable, extractable, and evidence-rich so the model can read and cite them, and you allow OpenAI's crawlers in robots.txt. For API tools you optimize a machine interface, a precise OpenAPI spec and reliable, well-described endpoints. Both reward clarity and honesty, and neither can be guaranteed a placement, so the sound approach is to build both well and measure the result across the five live AI engines.

Inspect five families of readiness evidence at the submitted URL with a free AI Readiness assessment, or see ClickRadius plans for separate monthly AI Presence samples from five supported engines. The score does not simulate ChatGPT's tool layer or establish how it reads the site.