A tool by Firecrawl · uses 4 tools · Marketing

Seasonal content calendar from search history

Use when an SEO lead or content strategist plans next quarter's editorial calendar and needs to know when each topic really peaks. Reads five years of search interest to find each topic's seasonal window, the rising questions to target, whether podcasts pick the topic up before search does, and reference facts to anchor hub pages, then writes a dated content calendar.

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Tools it calls

Each tool's price and track record across every agent today. Empty means the call worked and found nothing.

ToolPriceMedianSuccessEmpty

TrendsInterest over timePrice5 creditsMedian414 msSuccess95.8%Empty31.3%
TrendsRelated queriesPrice5 creditsMedian356 msSuccess95.8%Empty52.5%
ParticleEpisode counts over timePrice5 creditsMedian–Success–Empty–
WikipediaArticlePrice1 credits eachMedian195 msSuccess100%Empty20%

Instructions

What your agent follows when it runs this tool. Paste them into any assistant connected to Firecrawl.

# Seasonal content calendar from search history

For up to five topic keywords and one market. Pages need to be live and indexed before the ramp, not at the peak.

## 1. When each topic peaks

1. Chart each topic with @[Trends · Interest over time](alexandria:firecrawl-trends/trends/interest_over_time) (`keywords`, `geo`, `time=today 5-y`). Note the peak month in each year and how many weeks before it interest starts climbing. Carry forward a **publish-by date** per topic: ramp start minus your usual indexing lead time.
2. For each topic read @[Trends · Related queries](alexandria:firecrawl-trends/trends/related_queries) (`keyword`, `geo`, `time=today 3-m`). Rising queries become supporting articles; top queries become hub pages.

## 2. Lead indicators and facts

3. Check whether audio leads search with @[Particle · Episode counts over time](alexandria:particle/podcasts/episodes/timeseries) (`keyword_search` = topic, `published_after` = two years ago, `interval=month`). Months where episode counts climb before search interest mean the topic is discovered in podcasts first; plan social and audio promotion earlier for those.
4. For each hub page, pull definitions, dates and figures with @[Wikipedia · Article](alexandria:wikipedia/articles/lookup) (`name` = topic) so the page cites facts instead of inventing them.

## Write the content calendar

- **Calendar table:** topic, peak month, ramp start, publish-by date, page type (hub or supporting), target queries.
- **Rising angles:** the newest rising queries per topic, with the date range they were read for.
- **Lead indicators:** topics where podcasts move before search, with the months.
- **Evergreen vs seasonal:** which topics are flat enough to publish any time.
- **Sources:** cite each date and query to its capability. Trends is relative interest on a 0 to 100 scale, not search volume; say what could not be found rather than guessing.

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