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ga4 query audience export

ga4_query_audience_export

Read-onlyIdempotent

Query GA4 audience export rows with privacy defaults; include personal identifiers only by explicit opt-in.

Instructions

Query rows from an existing GA4 Audience Export. User/device identifiers are redacted by default; include them only through explicit opt-in.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum number of returned items or rows. The declared bounds and default apply; use pagination for additional results.
`offset`	No	Zero-based number of rows to skip before returning results.
`audienceExportName`	Yes	Full GA4 audience-export resource name returned by list_audience_exports.
`includePersonalIdentifiers`	No	Explicitly return user/device identifiers. False redacts identifier columns by default.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes	Original tool result: parsed JSON when the text is JSON, otherwise the text or multiple MCP content blocks. Provider fields depend on the selected query.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.1Sep 23, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With readOnlyHint and idempotentHint already declared in annotations, the description adds valuable behavioral context: user/device identifiers are redacted by default and require explicit opt-in. This is meaningful beyond what annotations alone convey and does not contradict 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?

Two sentences with no filler: the first states the core operation, and the second captures an important caveat. Every sentence earns its place and the critical opt-in detail is front-loaded.

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?

For a simple read-only query tool with fully documented parameters, an output schema, and safety annotations, the description plus schema covers everything an agent needs to call it correctly. Pagination and resource-name guidance are already provided in the schema.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds extra meaning by clarifying the default redaction behavior and explicit opt-in requirement, which directly informs how includePersonalIdentifiers should be interpreted.

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

Purpose4/5

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

The description uses a specific verb and resource ('Query rows from an existing GA4 Audience Export'), clearly indicating what the tool operates on. It is distinct enough from siblings like list_audience_exports because it targets export rows rather than the export list, though it does not explicitly compare itself to alternatives.

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?

The description establishes a clear usage context: use this when needing to query rows from an already-created GA4 Audience Export. It lacks explicit exclusions or named alternatives, so it stops short of a 5, but the context is unambiguous.

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