Trendzeist – Google Trends for content ideation

trendzeist-mcp

Turn Google Trends into your next 10 blog posts — in one call.

trendzeist-mcp gives your AI assistant ranked breakout / rising / evergreen topics, the questions people actually ask, an AEO opportunity finder that scores question clusters with trend, news coverage, Wikipedia presence and citability hints, plus interest curves, regional demand, real-time trends, who covers a topic in Google News and Wikipedia attention. Free, local, private. No API key, no account, no browser.

*SEO and AEO:* find the questions people ask — and answer engines answer — then make your site the source they cite. Measuring your own visibility inside ChatGPT / Perplexity / AI Overviews is out of scope; this server tells you *what to write*, not *who cites you*.

You:   Give me blog post ideas about home espresso for US readers.

Agent: → discover_topics(["espresso", "espresso machine"], geo="US") ← 1 breakout, 14 rising, 14 evergreen candidates with growth %, angles and 6 questions → mine_questions("espresso machine", geo="US") ← 30 long-tail questions: how-to 18, comparison 6, definition 4, listicle 2 → compare_keywords(["how to descale espresso machine", "best coffee beans for espresso"]) ← "Interest in 'how to descale espresso machine' rose 120% ... peaking 2026-08-30 (rising)." "1. How to Descale Your Espresso Machine (how-to, rising +120%, publish now) ..."

Built with

trendzeist-mcp is a thin MCP layer over [pytrends-modern](https://github.com/yiromo/pytrends-modern), which handles all Google Trends requests. Trendzeist adds the MCP tools and prompt, request throttling, a persistent disk cache, strict input validation, LLM-friendly JSON output and the ranked discover_topics workflow. Other runtime dependencies: mcp (official MCP Python SDK), pandas, platformdirs and requests. All MIT/BSD/Apache licensed. Google Autocomplete, Google News RSS and the Wikimedia REST API are called directly with requests (keyless).

Quick start

# any one of these

uvx trendzeist-mcp pipx run trendzeist-mcp pip install trendzeist-mcp && trendzeist-mcp docker run -i --rm ghcr.io/phalkmin/trendzeist-mcp

Claude Desktop — add under mcpServers in claude_desktop_config.json (macOS ~/Library/Application Support/Claude/, Windows %APPDATA%\Claude\, Linux ~/.config/Claude/):

"trendzeist": { "command": "uvx", "args": ["trendzeist-mcp"] }

Claude Code: claude mcp add trendzeist -- uvx trendzeist-mcp Cursor / VS Code / Codex: same command/args shape — see llms-install.md (written so you can paste it to an AI assistant and let it do the install).

Tools

Prompts: blog_ideas_from_trends(topic, audience, geo) (guided ideation), content_brief(topic, audience, geo) (titles, outline, FAQ, regions, publishers), answer_brief(question, geo) (a citable answer: direct answer, statistic, quote, sources, FAQ, schema) and weekly_review(seeds, geo, site_queries) (content meeting: publish now / plan for a date / refresh / retire). A ready-made agent skill lives in SKILL.md.

Output conventions

• Every result carries schema_version (currently 3; bumped only when a field is removed or changes meaning) and _meta with requests_made, cache_hit, cache_hits, cache_misses for that call — so the model knows when to slow down. _meta.archive_error appears only when the local archive could not be written (the tool result is unaffected).
• Explainable score. discover_topics.topics[] and aeo_opportunities.opportunities[] carry score (0-100) and score_breakdown with five components, each {points, max, reason}: demand 25 (questions found or top-query popularity), momentum 25 (direction_now + breakout / rising), freshness 15 (Google News, 0 when not fetched), gap 20 (vs. site_queries: gap 20 / partial 10 / covered 0; neutral 10 without them), aeo 15 (question-shaped; definition angle with no Wikipedia article). Compare scores within one call only.
• Observation archive. Every fresh fetch (not cache hits) of related queries, trend labels, trending searches and mined questions becomes one snapshot (unique id; an empty Google answer is a snapshot too) with normalised rows in archive.sqlite3 next to the disk cache (stdlib sqlite3, 365-day retention, opened lazily). Related-query snapshots hold the *complete* lists regardless of a caller's limit, so comparisons never invent drops. what_changed, trend_history and recurring_topics read it; trendzeist_status.archive reports it. Suspect rising queries never enter it. Archive errors never fail a tool. TRENDZEIST_ARCHIVE=0 turns it off.
• Site coverage is conservative but not literal. An aeo_opportunities cluster is covered only when *every* question in it is covered; a mix is partial (see coverage_counts), so exclude_covered can never hide an unanswered question. Tokens are compared after plural folding and a small tech synonym map (mac/macos, wp/wordpress, js/javascript …), so "claude code mac" is covered by a site query "claude code macos"; matched names the site query behind each verdict. Prefixes only match on 6-letter stems ("code" is not "codex"), and a one-word head term inside a longer site query ("claude" in "claude code macos") is partial, not covered. Feeding the raw Search Console variants still helps.
• angle is one of how-to | comparison | listicle | definition | verdict | news (or null) — the title format the query suggests. Pair it with evidence: how-to → steps + numbers, comparison → table + quotes, definition → cite a primary source, verdict (yes/no questions: "is it worth it", "does it work") → the verdict first, the deciding evidence, then caveats, news → dated publisher quotes.
• Anything silently adjusted is reported: clamped limits add a note, empty results add a reason.
• Two trend labels. direction compares the first and last third of the window (rising | falling | stable | new | no_interest | insufficient_data; new = no interest in the first third). direction_now compares the last quarter of the window with the quarter before it and answers "is it still growing *now*?". A topic that peaked mid-window reads direction: rising (or new) and direction_now: falling; the insight sentence says so. Composites (discover_topics, aeo_opportunities) always fetch the seed trend on today 12-m (echoed as query.trend_timeframe) and score on direction_now.
• Suspect rising queries. Search-manipulation campaigns attach one name to many unrelated "Breakout" queries. related_queries flags such items suspect: true with a suspect_reason (a flagged .com also flags the queries carrying its name as words); discover_topics moves them to suspect[]; aeo_opportunities ignores them. Google's is_breakout value is kept, only the trust changes.
• Off-topic related queries. Under niche seeds Google lists unrelated searches ("how to bake a cake" under an AI-tool seed) as breakouts from a tiny base and at values 0-3 in the top list. A breakout, or a top query below 10, that shares no word with the seed, the call's other seeds or the seed's on-topic top queries is off_topic: true (exact words after plural folding; generic words like make, work, best, github don't count). discover_topics mov