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scalably-io

Google Analytics 4 MCP

by scalably-io

ga4_batch_run_reports

Read-only

Run up to five Google Analytics 4 report queries in one round-trip, combining paired views like landing pages and referrers to reduce API calls and ensure atomic data retrieval.

Instructions

Run up to 5 GA4 reports in a single round-trip.

requests: list of run_report-shaped dicts. Same top-level parameters as ga4_run_report (minus property_id, inferred from the batch). Each entry accepts: dimensions, metrics, date_ranges, dimension_filter, metric_filter, order_bys, metric_aggregations, limit, offset, keep_empty_rows, currency_code.

Useful when the agent needs paired views (e.g. landing pages + referrers for the same window) and wants them atomically + under one quota call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestsYes
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

A4.6/5.0
Behavior4/5

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

Annotations cover read-only and open-world hints. The description adds meaningful behavioral detail beyond annotations: the 5-report limit, atomic execution, quota-call efficiency, and property_id inference. It does not describe failure modes, but the read-only annotation and output schema reduce the need for that.

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 description is compact and front-loaded with the core purpose. The request-shape explanation is structured as a list, and every sentence provides necessary invocation or selection guidance without filler.

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

Completeness5/5

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

Given the minimal schema, the description provides enough detail to construct valid batch requests and choose the tool appropriately. It covers request shape, accepted fields, top-level property_id, limits, and the atomic/quota benefit, while an output schema exists for return values.

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

Parameters5/5

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

Schema description coverage is 0%, and the requests item schema is just an open object. The description fully compensates by explaining that each request is run_report-shaped, listing all accepted parameters, and clarifying that property_id is inferred from the batch rather than repeated per request.

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 states a specific verb ('Run'), a bounded resource ('up to 5 GA4 reports'), and a clear distinguishing mechanism ('in a single round-trip'). It also references ga4_run_report, making the relationship to sibling tools obvious.

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?

It clearly says when to use it: when the agent needs paired views for the same window and wants them atomically under one quota call. It does not explicitly say 'use ga4_run_report for a single report,' but the batch framing and reference to the single-report tool make the intended division of labor clear.

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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