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End-to-End Data Analytics

Measure Analytics MCP

An open-source MCP that lets AI assistants audit and query GA4, GTM, BigQuery and Consent Mode — safely, in plain language.

Client
Open source · Apache-2.0
Type
Open-source AI tooling
Role
Creator & maintainer
Category
End-to-End Data Analytics ↗

7

Servers

52

Tools

Apache-2.0

License

Overview

Measurement audits are slow and repetitive: clicking through GA4, GTM containers, consent settings and BigQuery exports to find what's broken. Measure Analytics MCP turns that work into tools any AI assistant can use through the Model Context Protocol — seven focused servers, 52 tools, published on PyPI and built on a shared SDK.

Scope of work

01

MCP server design

02

GA4 Admin & Data API

03

GTM audits & guarded writes

04

Consent Mode v2 checks

05

BigQuery cost guardrails

06

Data-quality & alerts

01 · The challenge

Auditing a measurement stack means jumping between consoles and repeating the same checks for every property and container.

  • GA4, GTM, Consent Mode, server-side tagging and BigQuery each need their own expertise and tools.
  • Letting an AI touch production analytics is risky without strict limits on cost and writes.
  • Setup friction: credentials, project IDs and environment files for every source.

02 · The solution

Seven MCP servers on a shared Python SDK: BigQuery Analyst, GA4 Intelligence, Consent Mode Auditor, Data Quality, GTM Operations, Server-Side Auditor and an Alerts Engine. Safety is built in — read-only by default, SQL parsed with sqlglot so only read-only queries pass, a dry-run cost estimate before every query, and GTM changes that are simulated, require explicit confirmation and are never published. One OAuth login powers every server.

03 · Results

  • 52 tools across 7 servers, installable with pip install measure-analytics-mcp.
  • Full-stack audits in plain language from Claude, ChatGPT or any MCP client.
  • Cost-aware BigQuery: dry-run estimate before running, confirmation above a 1 GiB cap.
  • Open source on PyPI and GitHub, typed and tested, with a sibling MCP for paid media.

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