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scalably-io

Google Analytics 4 MCP

by scalably-io

ga4_run_access_report

Read-only

Builds an access audit report for a GA4 property, listing user emails and access counts over chosen date ranges to identify who-read-what, confirm service account activity, and support compliance audits.

Instructions

Audit log of who-read-what on a GA4 property (last 12 months).

dimensions (common): userEmail, accessedPropertyName. metrics: accessCount. date_ranges: list of {start_date, end_date} (YYYY-MM-DD or relative). Defaults to last 7d.

Use cases: identify usage of GA4 data by staff, confirm SA activity, compliance audits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
metricsNo
dimensionsNo
date_rangesNo
property_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful behavioral context: data retention of 12 months, default date range of 7 days, and the common dimensions/metrics. This goes beyond the annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The summary is one clear sentence, followed by compact parameter guidance and use cases. Every line adds value, with no filler or repetition of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Moderately complex tool with an output schema present, so return-value details are not required. The description covers the key constraints, defaults, and use cases. Minor gaps like property_id and limit semantics keep it from a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description partially compensates by explaining date_ranges format/defaults and the meaningful dimensions/metrics. However, it does not explain the required property_id parameter or the limit parameter, leaving part of the parameter semantics to inference.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Audit log of who-read-what on a GA4 property'. It clearly distinguishes this access-report tool from regular reporting siblings like ga4_run_report, and reinforces the purpose with named dimensions and metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit use cases are provided: identifying staff usage, confirming service account activity, and compliance audits. It does not name alternative tools or state when not to use it, but the audit-log framing makes the intended context clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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