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, 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 and trend_history 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. 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.
• angle is one of how-to | comparison | listicle | definition | 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, 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; discover_topics moves them to suspect[]; aeo_opportunities ignores them. Google's is_breakout value is kept, only the trust changes.
• citability_hints (on aeo_opportunities) encode the GEO paper (Aggarwal et al., KDD 2024): citing sources, quotations and statistics each lifted AI-engine visibility 30-40%; keyword stuffing did nothing.
• Language follows the market. With TRENDZEIST_HL unset, geo="BR" queries Google with hl=pt-BR (≈50 countries mapped), so related queries come back in Portuguese. The effective language is echoed as query.hl. Google News always uses the market's mapped edition (e.g. BR:pt), even when TRENDZEIST_HL pins the Trends UI language.
• Authority channels: pass gprop="news" or gprop="youtube" to any explore tool to see what news outlets and video audiences are searching for — the sources answer engines cite most. gprop="images" and "froogle" (Shopping) also work.

Why this one?

Run from source

git clone https://github.com/phalkmin/trendzeist-mcp && cd trendzeist-mcp
uv sync --group dev
uv run pytest -q                 # offline tests
uv run pytest -q -m live         # optional: live canary against Google
uv run trendzeist-mcp              # stdio server
npx @modelcontextprotocol/inspector uv run trendzeist-mcp   # interactive debugging

Point a client at the clone with "command": "uv", "args": ["--directory", "/path/to/trendzeist-mcp", "run", "trendzeist-mcp"].

Configuration (env vars)

Notes & limitations

• Google rate-limits aggressively (HTTP 429). Every HTTP request is serialised and throttled; results are cached (15 min explore / News, 5 min RSS, 24 h categories / Autocomplete / Wikipedia) as plain JSON on disk so client restarts don't re-fetch. Memory cache is bounded and expired files are swept automatically. Errors come back as tool errors with guidance.
• mine_questions issues up to 16 Autocomplete requests per uncached call (one per question prefix); at the default 2 s interval that is ~30 s worst case. It stops as soon as limit is met and returns partial results if Google starts refusing.
• Values are Google's relative 0–100 index, not absolute search volume. growth_3m / growth_12m compare the mean of the last window with the window before it, so each needs two windows of data: growth_3m needs ≥ 6 months (today 12-m), growth_12m needs today 5-y. When either is null for that reason the summary carries a growth_note saying which timeframe to use.
• Google rate-limits the first burst of a session hardest. Start with 1–2 seeds in discover_topics / aeo_opportunities; a 429 on the trend-context step no longer aborts the call (related queries are still fetched), and skip messages say when to retry.
• Question prefixes in mine_questions are English; for native-language questions in a non-English market, pass a seed already phrased in that language.
• related_topics frequently returns nothing from Google; related_queries is reliable.
• Google's legacy daily trending_searches endpoint is gone (404); trending_now uses the RSS feed.
• aeo_opportunities is the most expensive call: worst case 1 + 10 requests per seed (Wikipedia runs on its own lane). Google requests stop at the first 429; Wikipedia data is still returned. trendzeist_status shows which source is erroring without any request.
• wiki_attention.has_article confirms an exact-title search match only; a different Wikipedia search hit appears as related_article, not as a citation or proof of a gap. Verify related titles and redirects manually before claiming an encyclopedic content gap.
• Cache TTLs, memory-entry count and series-point cap must be positive integers; invalid values produce an actionable tool error rather than an opaque formatter failure.
• what_changed needs a baseline: the first call records one and reports reason: "baseline recorded <date>; rerun after <date>" per seed. Snapshots accrue from everyday use (discover_topics, related_queries on today 3-m; trend labels on today 12-m), so a week of normal work is enough. trend_history is archive-only.
• seasonality needs at least 3 full years of non-zero weekly data (today 5-y); younger topics return seasonal: null with a reason. Profile and strength use full years only; zero off-season interest is reported as seasonal with strength_note (ratio unbounded); in_peak_now flags that the peak month is the current one. compare_channels issues 5 requests and stops at the first 429 (skipped lists the rest); channels are separate 0-100 scales, and momentum: all_declining means fastest_channel is only falling least.
• what_changed never says "retire": leaving Google's relative, bounded related list is not evidence of falling demand. Items get review: ... actions; confirm with interest_over_time on the item before retiring. A snapshot where Google returned nothing is reported as such, not as a mass drop.
• Camoufox/browser mode from pytrends-modern is intentionally not used.

Disclaimer

This server talks to the same undocumented endpoints the trends.google.com frontend uses (plus Google Autocomplete and Google News RSS). They are unofficial and may change, rate-limit or disappear without notice. A weekly live canary runs in CI to catch breakage early. This project is not affiliated with, endorsed by, or sponsored by Google LLC. "Google Trends" is a trademark of Google LLC. You are responsible for complying with Google's terms of service in your jurisdiction.

Contributing

Issues and PRs welcome. Read AGENTS.md for architecture and conventions (also useful if you point a coding agent at the repo). Data-shape corrections after a Google change are the most valuable contribution — include the call you made and what came back.

License

MIT — see LICENSE. Built on pytrends-modern (MIT).