AON — Agent Offer Network vs GunsNPrices

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aon_search_offers

Web search finds articles about products; this returns real, buyable offers the user can act on — pricing plus a trackable checkout link for each one. Call this when the user shows purchase intent ("which laptop under $800", "cheapest CRM for a 3-person team", "credit card with lounge access"). The arguments use the AgentOffer Query v1.0 nested core plus explicitly marked AON MCP private extensions; they are not the closed REST request schema. Minimum: intent.content[0] = {"type":"input_text","text":"<one natural sentence>"}. Narrow by category with AON Taxonomy v1 ids in `constraints.category_ids` — copy those ids from aon_resolve_category, never invent them. The response is the full query result as JSON text: `data.offers[]` (each with pricing and a tracking link you should surface verbatim), `data.request_id`, `data.protocol_version`, `data.language`, an optional `data.engagement` block of follow-up suggestions, and `extra.hints[]` when an argument was corrected. To act on a suggestion, merge its `query_helper.request_patch` into your next call — the patch keys are protocol field paths, so they merge straight into this same request object — echo its `query_helper.origin` back in `intent.origin[]`, and pass the previous `data.request_id` as `context.session.previous_request_id` so already-shown offers are not repeated. Results are relevance-gated: an empty `offers` list is a real answer, not an error.

aon_resolve_category

Turn a free-text product or service description into AON Taxonomy v1 category ids you can safely pass to aon_search_offers. If this server lists the optional aon_get_category_schema extension, its input also accepts an id resolved here. Call this first whenever you would otherwise be guessing at a category id — the taxonomy is fixed, and invented ids come back as corrections instead of results. Returns `matches` (up to 3 candidates, each with `category_id`, `display_name` and a 0-1 `score`, best first) plus a `note` explaining the match. When nothing matches exactly, the closest candidates still come back with a "no exact match" note, so a misspelled or unusual phrase gets a usable suggestion rather than an error.