AI Answer Visibility data for agents

How an agent would call it

{
  "tool": "search",
  "arguments": {
    "query": "<your real query>",
    "sources": ["ai-visibility"],
    "runId": "<from run_begin>"
  }
}

Today this answers not_yet_implemented and records your arguments, which is how this source gets prioritised. Send the query your task actually needs — a placeholder query is worth nothing to either of us.

Routing guidance

What an agent reads to decide whether ai-visibility is the right source, and what to reach for when it is not.

Answers

Whether AI assistants mention you, and who they cite instead. One answer per engine carrying the cited domains and citation count, plus one result per cited source. Compare cited domains across engines and over time for share of voice.

Query shape

A natural-language PROMPT, not a keyword. Ask what a real user would ask — you are measuring what the engine answers, not what an index contains.

Cost

Metered per prompt, per engine. Narrowing to one engine roughly halves it.

Latency

Slow — around 145 seconds. Never put it in a latency-sensitive path.

Prefer instead

`google-serp` when the question is about Google’s AI Overview — far cheaper and far faster. `grounded-answers` for a free first look at AI visibility.

Cannot answer

Rankings, search volume, or what any engine other than ChatGPT and Perplexity says.

Caveat

Answers are non-deterministic. A single run is an observation, not a measurement — repeat before concluding.

Caveat

Programmatic results diverge substantially from what a signed-in human sees in a consumer chat UI. Treat this as a trend signal, never as ground truth about what users see.

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