Google Analytics 4 MCP

ga4_list_property_annotations

Read-only

Retrieve reporting annotations for a GA4 property to correlate metric changes with known events such as product launches, outages, or marketing pushes.

Instructions

List reporting annotations on a GA4 property.

Annotations are markers (user-added or Google-system) flagging important events on a timeline: product launches, outages, marketing pushes, so downstream analysis can correlate metric swings with known events.

Each annotation has: name, title, description, annotation_date, annotation_date_range, color, creator_email, system_generated.

NOTE: Uses Admin v1alpha since annotations are still alpha in April 2026.

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_property_annotations"
  }
}

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

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds genuine behavioral context beyond annotations: it warns that the tool uses Admin v1alpha because annotations are still alpha in April 2026, signaling an unstable API surface. It also enumerates the exact returned fields, though that partially overlaps with the existing output schema.

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 action is front-loaded in the first sentence, followed by three short paragraphs each adding distinct value: the conceptual explanation of annotations, the field list, and the alpha API version caveat. There is no filler, no repetition of annotation metadata, and no redundant content.

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-parameter list operation with an output schema and read-only annotations, the definition is thorough: it explains the niche resource concept, enumerates its fields, and flags the alpha API version. The only gaps are undisclosed behavioral details such as pagination behavior and possible result limits.

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?

Schema description coverage is 0%, so the description must compensate, but it only provides minimal context ('on a GA4 property'), leaving property_id's format and origin undocumented. The burden is softened because property_id is a single self-explanatory parameter whose name conveys its meaning.

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?

States a specific action and resource: 'List reporting annotations on a GA4 property.' The explanation of what annotations are (markers for product launches, outages, marketing pushes) gives the agent a concrete model of the resource, which distinguishes this from every sibling list_* tool (custom_metrics, key_events, audiences, etc.), none of which mention annotations.

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 description implies when the tool is useful — 'so downstream analysis can correlate metric swings with known events' — which gives the agent situational context for selecting it. However, it never explicitly states when to use this tool versus the 16 siblings, nor names an alternative for related resources such as key events or custom metrics.

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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