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The commerce API for AI shopping agents
An agent that returns a price has done a lookup. An agent that says whether the price is good has done the job. The gap between those is a history the agent does not have and cannot build inside a conversation.
AI shopping agents
Verdicts, not readings
price_context returns excellent, good, typical, poor or flat with the percentile and window behind it, so the agent can state its reasoning rather than assert a conclusion.
AI shopping agents
Memory across sessions
Preferences, constraints, rejections and past purchases persist per namespace. The next conversation starts from what is already known instead of re-interviewing the user.
AI shopping agents
Metered per tool
One prompt fans into dozens of calls with nobody approving each one. Each tool declares its cost so an agent can budget, and a cheap metadata lookup never bills like a live cross-seller compare.
Endpoints that do this
Where it lands in the API.
MCP server
Six tools, not thirty endpoints. A chatty server makes an agent burn context stitching fragments and gives it room to hallucinate the join.
Price context
Is this actually a good price? Percentile against the product's own history at that seller, time-weighted, with low, median and high.
Recall
Semantic recall scoped to one namespace. Ask what matters for the decision in front of you rather than replaying an entire history into context.
Questions
Common questions.
Why not just let the agent browse?
A browsing agent sees one page at a time, has no history, and cannot tell whether the number in front of it is high or low. It also cannot answer the question without spending a great deal of context to still get it wrong.