Google data (Trends, YouTube, Ads Transparency, Play) MCP server

google-data-mcp

<!-- mcp-name: io.github.ducnhd/google-data-mcp -->

An MCP server for four Google data surfaces that answer to plain HTTP — no API key, no browser, no scraping service:

Read this before you install

Everything leaves from your machine's IP, and Google's per-IP budget accumulates over hours. This is not a caveat buried at the bottom; it is the main thing that decides whether this server is right for your job.

Measured: an unpaced burst was rate-limited at request 93, and after roughly 130 paced requests spread over hours the same address was refused on the first request of a fresh session. While writing this server, both the author's IPs — a home connection and a datacenter VPS — ended the day refused by Google Trends.

So this server caches every result on disk (~/.cache/google-data-mcp) and paces its requests. That is enough for interactive use: asking an agent a handful of questions, exploring a topic, checking a competitor. It is not enough for bulk work, and no amount of local code can make one IP behave like many.

If you need volume, the same clients run behind rotating proxies as Apify Actors: Google Trends · YouTube · Ads Transparency.

What is deliberately missing

YouTube transcripts. They look available — the watch page still lists caption tracks — but api/timedtext returns zero bytes and /youtubei/v1/gettranscript answers Precondition check failed even when sent the page's own INNERTUBECONTEXT and visitorData, and the ANDROID and IOS player clients are refused the same way. A transcript tool here would be a promise this server cannot keep, so there isn't one.

Install

pipx install google-data-mcp
# or: pip install google-data-mcp
# or, without installing anything: uvx google-data-mcp

Then register it with your MCP client. For Claude Code:

claude mcp add google-data -- google-data-mcp

Or by hand, in an MCP client config:

{
  "mcpServers": {
    "google-data": {
      "command": "google-data-mcp"
    }
  }
}

Notes on the data

Shortened. The full README is on GitHub.