Operators who run Amazon, Shopify, or marketplace SKUs often search for product demand API, Amazon trend data for FBA, or Google Shopping interest MCP. Trends MCP exposes Amazon product search interest, Google Shopping series, TikTok hashtag curves, and Google Search demand inside the same tool calls an assistant already understands.
Retail MCP guides often focus on carts, refunds, and catalogs. Those integrations matter for operations. They rarely answer whether demand for a phrase is steepening before inventory commits. Commerce teams therefore keep a parallel bookmark folder for Trends, seller dashboards, and social clips.
A radar is a small set of indicators reviewed on a fixed cadence. For commerce, the inputs are usually pull-oriented search on marketplaces, intent-heavy shopping queries, and attention spikes on short video. Trends MCP maps those inputs to typed responses so an agent can chart them without building custom scrapers.
Pick one hero keyword per SKU variant. Request growth spans that match planning horizons (for example 30D and 3M). Store the JSON snapshot with the date. When the next run lands, compare direction first, then magnitude. Sudden lifts on TikTok with flat Shopping can warn about hype that logistics cannot support; the reverse can signal durable purchase intent with weak creator coverage.
Details for each source string appear on data sources. Multi-source growth comparisons are documented for the API shape on trendsmcp.ai/docs.
This page stresses system design for operators who already found ecommerce product research and want a repeatable radar. Pair it with Amazon search trends and Google Shopping trends when teammates need source-specific depth.
The free tier caps monthly calls; heavy category scans should map to a paid plan after a pilot. The accounting should live next to ad spend because both buy signal latency.
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