Papyras Product Catalog

Page through the full product catalog sequentially (ordered by when each product was added), rather than searching for anything specific. Useful for bulk export or systematically walking the entire catalog. Defaults to in_stock only; pass availability="all" to include every status. Increase `offset` to get subsequent pages.

Fetch full detail for a single product by its UUID, including description, material/color/size, full enrichment fields (explanation_long, carousel_label, gift-friendliness, price percentile, rating), current price/availability, and the affiliate purchase URL. Returns full detail regardless of stock status -- this is a direct lookup, not a discovery search.

Fetch multiple products at once by their UUIDs (max 100 per call). Useful when an agent already has a list of candidate product IDs (e.g. from a previous search) and wants full current data for all of them in one call instead of N separate lookups. Returns `results` for found products and `not_found` for any IDs that don't exist in the catalog.

Fetch products related to a given product (similar, accessory, bundle, or alternative relationships). Currently returns an empty list for all products since relationship data hasn't been generated yet -- calling this will not error, it just won't return anything useful until that pipeline is built.

Search the Papyras product catalog using hybrid structured + semantic search. Pass `query` for free-text/natural-language search -- longer, natural phrases (e.g. "pressure washer for patio") retrieve more accurately than bare single words (e.g. "patio"), since the embedding model has more context to place the query vector precisely. Combine with structured filters (category, brand, price range, intent_tag, use_case_tag, audience_tag) to narrow results further. Defaults to showing only in_stock products. Pass availability="all" to see every status, or a specific value (in_stock | out_of_stock | preorder | discontinued) to filter to just that one. Returns a dict with `results` (list of product summaries including name, price, affiliate_url, tags, and a short explanation of why the product is relevant), plus `count`, `limit`, `offset`, and whether semantic search was actually used.