Google Analytics 4 MCP

ga4_list_custom_dimensions

Read-only

Lists custom dimensions configured on a GA4 property with parameter names, display names, scope, and descriptions. Check available custom fields before running reports or querying metadata.

Instructions

List custom dimensions configured on a GA4 property.

Each entry contains the parameter_name (how you query it, use prefix 'customEvent:' or 'customUser:' when passing to get_metadata / run_report), display_name, scope (EVENT|USER|ITEM), and description. Essential grounding before running reports that reference custom fields.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "property_id": "<property_id>"
    },
    "name": "ga4_list_custom_dimensions"
  }
}

Input Schema

Name	Required	Description	Default
`property_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 8, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds valuable context about what each entry contains and how the parameter_name should be prefixed when used in get_metadata or run_report. It does not mention pagination or limits, but for a read-only list tool this is acceptable.

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, front-loaded with the core action, and every sentence adds useful information. The prefix guidance and entry content summary are directly actionable without adding fluff.

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?

For a low-complexity list tool with an output schema, the description covers the main purpose, the contents of the response, and the downstream usage of the returned data. It is nearly complete, though it could briefly mention where property_id comes from or address potential pagination.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not meaningfully elaborate on the property_id parameter beyond calling it a GA4 property. The parameter name and title are already in the schema, so the description adds little semantic value for this single required argument.

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 and resource: 'List custom dimensions configured on a GA4 property.' It is clearly distinct from sibling tools like ga4_list_custom_metrics and ga4_list_key_events by naming the exact entity being listed.

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 provides clear usage context: it is 'Essential grounding before running reports that reference custom fields.' It does not explicitly exclude alternatives or state when not to use it, but the context is strong enough for an agent to know when this tool is relevant.

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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