Grounding LLM research reports with trend JSON

What changes when appendices include machine-readable trends?

Reviewers can re-run the same call and expect the same shape. Trends MCP exposes get_trends, get_growth, and get_top_trends with field names documented on trendsmcp.ai/docs, so Git diffs show whether a memo refreshed its inputs. Teams already experimenting with RAG should read trend data for AI RAG for storage ideas.

How should analysts chain search, news, and Wikipedia pulls?

Start with Google Search or Google News for the macro phrase, add Wikipedia page views when the topic has a canonical article, and finish with news volume when coverage intensity matters. Social sources enter when the question involves creators or retail virality. Each hop should cite its own JSON block to avoid blending incompatible cadences.

Which prompt habits keep assistants honest?

Require "Using TrendsMCP" in the first instruction line and forbid the model from replacing missing data with estimates. When a response returns data_unavailable, the draft should say so explicitly. LLM native trend research workflow lists additional guardrails for mixed human and agent teams.

Where do ChatGPT connectors fit versus Python post-processing?

ChatGPT suits interactive drafting with attached JSON, while Python services suit scheduled memos that land in email inboxes. Both approaches read the same POST body documented for REST callers. News sentiment data explains how sentiment fields differ from raw volume when finance or policy teams read the output.