Google Search demand data for AI agents

Raw, structured Google Search demand data delivered directly to your AI. No API keys, no quotas, no scraping. Just query a keyword and get normalized volume, historical series, and growth signals back in seconds.

get_trends

Export the raw normalized Google Search time series for any keyword - each weekly data point includes date, normalized value (0-100), and absolute volume estimate for custom analysis or charting.

get_trends(keyword='cloud security', source='google search', data_mode='weekly')

get_growth

Compute structured Google Search demand growth over any window. Pass multiple periods to get 1-month, 3-month, and 1-year growth in a single response for trend velocity analysis.

get_growth(keyword='cloud security', source='google search', percent_growth=['1M', '3M', '1Y'])

get_ranked_trends

Retrieve a ranked feed of the top Google Search queries by volume or growth - useful for programmatic keyword discovery without a seed list.

get_ranked_trends(source='google search', sort='latest_value', limit=100)

get_top_trends

Pull what is trending on Google right now - ideal for news monitoring, content calendar planning, or spotting sudden demand spikes in your category.

get_top_trends(type='Google Trends', limit=30)

Common questions

JSON with a time series array (date + normalized value + absolute volume), a summary object with growth metrics, and a data quality score indicating coverage reliability.
No. Trends MCP handles all source connectivity. You connect once via the MCP config snippet and your AI can query Google Search data immediately.
Volume estimates are derived from normalized Google Trends data combined with third-party search volume calibration. They are directionally accurate for trend analysis but should not be treated as exact query counts.
Yes. A common pattern is: query a set of product or company keywords, rank them by search demand growth, and identify which has the strongest consumer interest signal.