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Target outcome
An agent-readiness roadmap for your highest-value pages
Audit access
Map extraction
Locate gaps
Build the roadmap
Run in Claude and Run in ChatGPT open a new browser tab with the prompt already typed. Nothing is sent until you submit it.
The queue at the end of this workflow has one row per machine-written search where competitors show up and you rarely do. Those searches are the ones detected inside your monitored answers, most of them carrying the prompt that triggered them, and everything here is built from that record.
Agent readiness usually gets filed as a markup chore: add schema and wait. The layer nobody opens is the query layer. Handling a comparison or a purchase question, an assistant expands the request into sub-queries of its own and answers from what they return. Those sub-queries are visible, and some surface your competitors and never you. That gap is measurable today.
What it will not tell you is whether an agent can complete a task on your site. Nothing here loads a page, renders JavaScript or walks a checkout. Those checks belong to your QA; mixing them in lets a manual result pass as observed data.
1. Read the searches the machine wrote. get_query_fan_out returns web search queries detected inside monitored answers, each with its appearance count, distinct answers, your presence rate, the direct competitors present, the prompts it came from, and a priority the tool computes itself: very-high when competitors appear and your presence is under 20 percent, high when competitors appear and presence is under 50 percent, medium when presence is under 50 percent without competitors, low otherwise.
2. Find where an agent could land. get_page_presence sorts your pages into WORKING at three or more observed citations, NEEDS_ATTENTION at one or two, NOT_CITED at zero, DISCOVERED for a cited page outside your monitored set. Against step 1 it answers one question per query: does a destination exist, or must one be created?
3. Check the buying surfaces. get_shopping_insights returns detected products, the stores carrying them, average ratings, and a store opportunity list limited to stores carrying competitor products, whether or not they carry yours. No prices, no product attributes: it shows where you are absent, never at what price.
4. Assemble the queue. One row per very-high query: query text, originating prompts, presence rate, competitor count, the destination that exists today, and the single change proposed.
Work in this order. Store opportunities first if you sell through third parties, since getting listed is an operation with a known result, not a bet on retrieval. Then very-high queries by appearance count, highest first. The high tier waits until very-high is empty.
Two very-high queries with the same count: take the one whose topic already has a NEEDS_ATTENTION page. Turning one or two citations into three is shorter than earning the first.
An empty step is still an answer. No detected queries means the providers answered without searching in that window, not that agents ignore you, and the queue comes from steps 2 and 3. No shopping data means your prompts are not the buying kind, a portfolio finding rather than a readiness verdict.
Audit how ready my site is for assistants that search and compare on a user's behalf. Use only what the tools return.
Call list_brands. If more than one brand comes back, ask which to use before pulling data.
1. get_query_fan_out over 30 days, limit 100. Report the unique query count, my average presence, and every query with its appearance count, distinct answers, presence rate, competitor count, priority and originating prompts. Flag results left unfetched. If nothing comes back, report that the monitored answers recorded no web searches in that window, never as agents ignoring my site, and carry on with steps 2 and 3.
2. get_page_presence, limit 100. Group my pages by status: WORKING is three or more citations, NEEDS_ATTENTION one or two, NOT_CITED zero, DISCOVERED cited but outside my monitored set. For each very-high query, name the page that would serve it today or state that none exists. No pages at all means an empty inventory, not zero citations.
3. get_shopping_insights over 30 days: products, stores, average ratings, store opportunities. If a listed store is not one I sell through, ask me which resellers we actually work with before calling it a listing gap. If nothing comes back, say my prompts do not cover buying questions and skip the layer rather than guessing.
4. One row per very-high query: query text, originating prompts, presence rate, competitor count, existing destination and status, the change you propose, the owner it needs. Mark every row where the destination has to be created from nothing.
5. Order the work: store opportunities first if I sell through third parties, then very-high queries by appearance count descending, then the high tier only once very-high is empty. Break ties on equal counts in favour of the query whose topic already has a NEEDS_ATTENTION page.
Never say Qwairy tested a page, rendered it or completed a task on it. List what my QA must verify by hand separately, outside the ranked queue. Do not promise any change will produce a citation.
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