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GermaniU

ga4-seo-mcp

by GermaniU

run_realtime_report

Read-only

Generate a realtime Google Analytics 4 report for the last 30 minutes. Use it to confirm a new event fires in production after a deploy.

Instructions

Realtime report for the last 30 minutes.

Useful to confirm that a new event fires in production right after a deploy.

Args: metrics: default ['activeUsers']; 'eventCount' also works. dimensions: default ['eventName']; also 'unifiedScreenName', 'country', 'deviceCategory'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
metricsNo
dimensionsNo
property_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

readOnlyHint and openWorldHint already cover the safety profile, and the description usefully adds the 30-minute freshness window as a behavioral constraint. It says nothing about the realtime API's tighter quotas, auth requirements, or the non-final/unsampled nature of realtime data, so the added context is modest against the lower annotated baseline.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The scope statement is front-loaded in a single clean sentence, followed by the use case and a compact Args block. No filler, though the Args section is raw list formatting rather than tight prose.

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?

An output schema exists, so return values need not be described, and annotations handle safety. Combined with the explicit time window, defaults, and use case, the definition is nearly sufficient — the only gaps are the undiscussed property_id and limit parameters.

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 coverage is 0%, so the description must carry parameter meaning, and it does supply real value for metrics and dimensions — defaults ('activeUsers', 'eventName') and valid alternatives that the bare schema (default null) does not reveal. However, the required property_id and the limit parameter receive no explanation at all, leaving half the parameters undocumented.

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?

States a specific verb and resource with a hard scope boundary: a realtime report covering the last 30 minutes. The 'realtime' framing implicitly separates it from the historical run_report sibling, but it never names that contrast explicitly, so the differentiation is left to inference.

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?

Gives a concrete triggering scenario ('confirm that a new event fires in production right after a deploy'), which tells an agent when this tool is the right call. It stops short of stating when-not to use it or naming run_report as the alternative for historical data.

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