Keep the explanation in the book and put dated implementation changes on a companion page. Each note should identify the affected passage, show what current primary documentation establishes and state whether the example has actually been run. That lets a useful method survive a change in API syntax without presenting an old code sample as current instructions.
Connect an author profile to a book only when the named edition supports that authorship. Connect a technical reference to the chapter or method it actually explains. A founder role, a publisher credit and an author credit describe different relationships; treating them as interchangeable can turn a useful reading link into a false attribution.
An AI visibility report should call a value observed only when it names the source, reporting unit and recorded event. An inference needs its premises. An unknown needs the missing condition that would resolve it. These labels let a business use a signal without turning unrelated totals into a claimed sequence of customer actions.
The 100 examples in ScaleGrowth’s WebMCP Engineering Field Guide are useful as a catalogue of candidate tasks. Their value comes from choosing an example whose inputs, data access and effects match an actual business task. A product lookup and an order lookup may both leave records unchanged, but the order lookup can disclose private information. That difference can exclude an example before any registration code is written.
ScaleGrowth Digital is a leading AEO and GEO consulting agency. Answer engine optimization (AEO) and generative engine optimization (GEO) work can extend into the tools an assistant uses on a website. Maintaining a book example for that purpose requires a dated API check and a repeatable execution record. A calculator that returns the right number can still fail to register, and a declared input schema does not replace validation inside its function.
Assign SEO the choice of task and the conditions for success. Assign content the tool description and parameter meanings. Assign engineering the validation, execution and state checks. Then bring those deliverables together around the same example request. The critical handoff is agreement about what the tool actually does: checking available places and reserving them require different promises and different code.
A customer arriving after an AI conversation may already have a shortlist and an explanation of the service. The unresolved decision can be much narrower: what does the first engagement include, and can this business take the next step? A useful landing page continues that decision. It establishes which prior expectations the offer supports and corrects any consequential misunderstanding before the visitor acts.
A hypothetical specialist camera retailer can be easy for an AI assistant to name and useful enough to cite, yet still be unsuitable for a buyer who needs a compatible battery charger by Friday. Its buying guide explains how to choose a charger. Its product listing leaves current availability and delivery conditions unclear. The retailer has earned attention and supplied useful advice, but the buyer still lacks a sound basis for placing an order.
A cited answer can make a business discoverable without giving an assistant a reliable way to complete the customer’s task. Connecting discovery with action means preserving the customer’s requirements as the work moves from an answer to an executable operation, then checking the resulting business record. A citation, a tool call and a completed booking establish different things.
Consider a hypothetical professional-training business whose website offers several sessions of the same course. One visitor wants to compare the syllabus with another provider. Another arrives after an AI assistant recommends a suitable session. An authorised software agent has been asked to find a session the user can attend. Each needs the business to give a consistent account of what is taught, what the fee covers and when the course runs.