AI agents can discover products through web search, pages they browse, third-party sources, directories, or tools already available in their environment. There is no single directory that every agent checks. The route depends on the user’s task and the agent’s access.
Discovery is a chain, not a single ranking
An agent trying to solve a task may first search for the category, then investigate a shortlist, then open documentation or a booking page. It can return to search when a source leaves a question unanswered. Your homepage may be only one stop, or it may never be opened.
For example, Google describes query fan-out in its AI features: related searches across subtopics and sources can help construct an answer. This is a documented Google behavior, not a claim about every assistant. Google’s AI features guidance.
Five routes worth mapping
| Route | What introduces you | What to prepare |
|---|---|---|
| Web search | A relevant result for a customer task | A useful, accessible page with a clear next step |
| Third-party evidence | A review, comparison, reference, or cited source | Accurate identity and claims the source can substantiate |
| Documentation | A recipe, API reference, or integration example | Inputs, outputs, limits, authentication, and completion |
| Directories | A category or product listing | Correct metadata and a working destination |
| Connected tools | An installed or configured integration | Descriptive operations and reliable task completion |
These routes overlap. A directory can lead to documentation; a review can lead to a product page; a search can uncover a tool listing. A listing is useful only if a relevant system or user actually encounters it.
A registry is not automatic installation
The MCP Registry publishes server metadata for clients and aggregators to consume. It is a distribution surface, not a promise that a particular assistant will install or call your server. A host can curate its own catalog or require an operator to connect a server. MCP Registry overview.
The same distinction applies to tool search. An application may search a set of tools it has made available; that does not imply a search of every API on the internet. OpenAI documents tool search as a way to discover and load deferred tools configured for an application. OpenAI tool search.
Start with a route hypothesis
Write a testable sentence: “A coding agent helping a developer extract invoice line items can encounter our task guide through web search, then reach our trial and complete an extraction.” Name the source page, intended query, next step, and expected output.
Test both discovery and use. In a discovery test, do not provide your brand name or preinstall your integration. In a separate adoption test, deliberately provide the product and examine whether the agent can use it. The second test reveals friction, but cannot prove the first route works.
What success looks like
A credible record includes the user task, environment, sources retrieved, page or tool selected, and observable result. A screenshot of an answer mentioning the company is evidence of a mention in that run. It is not evidence of an independent customer, referral, or sale. Keep those outcomes separate as you improve the path.
Primary sources
Put the next step to work.
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