MCP Google Analytics Server

ga_get_metadata

Read-onlyIdempotent

Retrieve the full list of available dimensions and metrics metadata for a GA4 property to identify valid fields for report queries.

Instructions

Get available dimensions and metrics metadata for your GA4 property.

⚠️ TOKEN OPTIMIZATION: This returns ALL available dimensions and metrics. Response can be large (~500+ items). Use sparingly and cache results when possible.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ga_get_metadata"
  }
}

Input Schema

Name	Required	Description	Default
`propertyId`	No	GA4 property ID to query (overrides the default GA_PROPERTY_ID). Accepts "123456789" or "properties/123456789". Use ga_get_account_summaries to discover IDs.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.3.1Aug 27, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / propertyId

Added value: +{
+  "description": "GA4 property ID to query (overrides the default GA_PROPERTY_ID). Accepts \"123456789\" or \"properties/123456789\". Use ga_get_account_summaries to discover IDs.",
+  "type": "string"
+}

Changed1 schema field changedv1.1.1Aug 6, 2026
- addedInput schema / $schema
```
Added value: +"http://json-schema.org/draft-07/schema#"
```
First observedv1.0.1Jul 30, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond that: the response is intentionally complete ('ALL available dimensions and metrics'), can be around 500+ items, and should be cached/sparingly used. This is valuable operational disclosure.

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 two short blocks: a one-sentence purpose followed by a focused warning. It is front-loaded, contains no filler, and every sentence contributes either to selecting the tool or using it responsibly.

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 single-optional-parameter read-only tool with fully documented schema, the description gives the essential context: what the tool returns, that the response is large, and that results should be cached. The absence of an output schema means some return-format details are unspecified, but this is a minor gap given the simplicity of the operation.

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?

The input schema already documents propertyId thoroughly, including accepted formats and a discovery hint via ga_get_account_summaries, and schema coverage is 100%. The description itself adds no parameter-level detail, so the baseline of 3 applies.

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+resource: 'Get available dimensions and metrics metadata for your GA4 property.' It clearly distinguishes this from siblings such as ga_list_custom_dimensions and ga_list_custom_metrics, which concern custom definitions, and ga_run_report, which executes queries.

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

Usage Guidelines3/5

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

The token-optimization note tells the agent when to be cautious and to cache results, which is useful operational guidance. However, it does not explicitly state when to choose this tool over alternatives or when not to use it, so selection guidance is only implied by the purpose.

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