Seasonal trend analysis for AI assistants

Every product, keyword, and category has a seasonal rhythm. Trends MCP gives your AI 5 years of weekly data to map those patterns precisely - so you plan around evidence, not gut feeling.

Seasonal trend analysis answers one question: is this category running ahead of or behind where it was at the same point last year? That comparison - current demand versus prior-year baseline - is more actionable than absolute levels, which fluctuate with broader market conditions.

Trends MCP gives AI assistants access to 5 years of weekly historical data for any keyword across 15+ platforms. Point-to-point year-over-year comparisons, custom date baselines, and multi-source seasonal validation are all available in natural language from Claude, Cursor, ChatGPT, or any MCP-compatible AI client.

Why seasonal baselines matter more than absolute values

A keyword showing a normalized Google Search value of 60 is meaningless without context. Is 60 high for this keyword in March? Was it 80 in March last year? Was it 30 two years ago? The absolute value tells you nothing. The year-over-year comparison tells you whether the season is running strong or weak.

Most trend tools show you absolute values and let you eyeball a line chart. Trends MCP calculates the comparison directly: the get_growth tool with custom date objects lets you compare any recent reading to any prior-year baseline, returning percentage change and direction as a queryable result.

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