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, a title angle per idea, plain-English trend insights, interest curves, regional demand and real-time trends. 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.

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

Prompt: blog_ideas_from_trends(topic, audience, geo) — a guided ideation workflow.

Output conventions (since 0.3.0)

• Every result carries schema_version (currently 1; 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.
• 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.
• 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.
• 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, 5 min RSS, 24 h categories / Autocomplete) 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 and are null when the timeframe is too short (use today 12-m / today 5-y).
• 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.
• 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 for mine_questions). 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).