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, interest curves, related searches, regional demand and real-time trends. Free, local, private. No API key, no account, no browser.

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 %
       → compare_keywords(["how to descale espresso machine", "best coffee beans for espresso"])
       "1. How to Descale Your Espresso Machine (rising +120%, publish now) ..."

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.

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) 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.
• Values are Google's relative 0–100 index, not absolute search volume.
• 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. 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).