Teams building RAG and agent stacks often ask how to ground answers in fresh demand signals instead of only static documents. Trends MCP supplies time-stamped series and growth summaries your retrieval layer can fetch at query time, then cite like any other structured tool output.
Classic RAG retrieves chunks, then answers. Agentic RAG adds tools. A trend tool call should run when the question mentions momentum, seasonality, or breaking topics. After the call, concatenate a short facts block (source, keyword, latest value, date) above retrieved docs so the model weights both.
For warehouses, store each series as rows keyed by keyword, source, and date. For vector stores, embed short summaries ("TikTok hashtag #xyz peaked week of 2026-03-10") rather than raw numbers alone. Refresh embeddings when new points change slope materially, not on every daily tick.
When the user compares platforms, run separate calls per source. Merge in the answer with clear labels. Normalized 0 to 100 scores help cross-platform reads but still describe relative shape inside each pipeline, not universal units across unrelated signals.
Trend data can be missing for niche phrases. Sentiment scores need careful language in regulated industries. Agents should state uncertainty when samples are thin. Trends MCP provides inputs; policy layers decide what the assistant may claim.
Tools for this workflow
get_trendsRetrieve normalized history for a entity string before summarizing it beside PDF chunks in the same prompt.
get_trends(keyword='openai', source='google search')
get_growthReturn percent change objects the agent can sort when a user asks which competitor accelerated the fastest this year.
get_growth(keyword='openai', source='google search', percent_growth=['6M', '12M'])
get_top_trendsOffer a same-day leaderboard snippet when the user asks what is trending now on Wikipedia or X.
get_top_trends(type='Wikipedia Trending', limit=15)
FAQ