EXPOSE YOUR CATALOG AS AN MCP TOOL OR ASSISTANTS WON’T SEE YOUR PRODUCTS
ChatGPT and other assistants increasingly shop via tools that query real product catalogs, not by scraping your site. A detailed walkthrough shows how assistan...
ChatGPT and other assistants increasingly shop via tools that query real product catalogs, not by scraping your site.
A detailed walkthrough shows how assistants trigger tool calls to a Model Context Protocol (MCP) server to run product search and return live price and stock, shifting “search” to API-backed catalogs rather than HTML pages how it works. For WooCommerce and similar stacks, the playbook is to publish a clean product-search API, document it in a simple discovery file like llms.txt, and keep structured signals consistent (WooCommerce guide, llms.txt explainer).
For agent workflows that need broader web context, use a SERP API to feed results as JSON instead of scraping HTML; it is more stable and easier for the model to use SERP JSON guide.
Assistants route product requests to tools, so catalogs without tool endpoints or clear discovery signals get skipped.
Owning a fast, accurate product-search API makes your prices and stock the source of truth in AI answers.
-
terminal
Stand up a minimal MCP product_search tool on a staging catalog; verify an assistant can filter by price, color, availability, and return canonical URLs.
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terminal
Measure latency, cacheability, and price freshness under tool call bursts; add tracing to spot slow joins and N+1 queries.
Legacy codebase integration strategies...
- 01.
Layer an MCP server over existing product services; map SKUs, variants, regional pricing, and inventory to a stable search schema.
- 02.
Publish a lightweight llms.txt and a read-only product JSON endpoint; add auth, rate limits, and OTel spans without touching checkout.
Fresh architecture paradigms...
- 01.
Design a canonical product-search contract first-class for agents: deterministic filters, stable IDs, and pagination with total counts.
- 02.
Plan discoverability early: structured JSON responses, consistent attributes, and a maintained llms.txt pointing to your API.
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