Shopping search demand assistants can read as time series

Purchase intent often concentrates in Google Shopping before it spreads across generic web search. Source google shopping gives assistants a clean series for product and category phrases so merchandising, pricing, and retail analytics briefs cite the same JSON the API returns.

Shopping intent is a distinct signal

Generic web search mixes research and purchase. Shopping vertical queries lean toward comparison and checkout mindset. Giving assistants source: "google shopping" aligns recommendations with that intent layer documented in Data Sources.

Typical assistant workflows

E-commerce operators ask for get_growth across google shopping, amazon, and google search to see whether demand is discovery-led or transaction-led. Finance-style briefs may pair the same call with wikipedia or news volume for narrative risk.

Honest limits

Assistants should state that normalized scores aid comparison but do not replace inventory, margin, or conversion data from the storefront. Always pass through the dates returned in each point when summarizing.

Related pages

Product marketing copy: Google Shopping trends. Broader commerce research: E-commerce product research. MCP hub: MCP trend tools for assistants.

get_trends

Trace multi-year shopping interest for a SKU family before assortment planning.

get_trends(keyword='robot vacuum', source='google shopping', data_mode='weekly')

get_growth

Score category candidates by quarter and year growth for retail decks.

get_growth(keyword='robot vacuum', source='google shopping', percent_growth=['3M', '12M', 'YTD'])

get_ranked_trends

List fast-rising shopping queries when the user asks what product themes are accelerating.

get_ranked_trends(source='google shopping', sort='yoy_pct_change', limit=30)

get_top_trends

Pull Amazon best-seller feeds when the brief needs live commerce leaderboards alongside shopping search history.

get_top_trends(type='Amazon Best Sellers Top Rated', limit=20)

Common questions

google shopping with a space, matching the keyword sources table on trendsmcp.ai/docs.
Product names, model families, category phrases, and comparison queries people type when they intend to compare or buy.
Use get_growth with comma-separated sources google shopping and amazon on the same keyword when the product language lines up. Interpret differences as channel mix, keeping in mind each pipeline has its own normalization.
Weekly series separate durable demand from launch spikes. Pair with 3M growth when buyers ask whether a category is still expanding.
Get Shopping search demand assistants can read as time series in 30 seconds
Free tier includes 100 requests per month. No credit card required. Works with Claude, Cursor, ChatGPT, Raycast, and every MCP client.
Get your free API key