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leonardosepulvedat

MCP Google Analytics Server

ga_batch_run_reports

Read-onlyIdempotent

Execute multiple Google Analytics 4 report requests in one batched API call, reducing overhead and simplifying data retrieval.

Instructions

Run multiple reports in a single request.

⚠️ TOKEN OPTIMIZATION: Can return LARGE amounts of data. Limit to 2-5 reports per batch. Each report should have small limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestsYesArray of report requests (same format as ga_run_report)
propertyIdNoGA4 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. Dates show when Glama detected each change.

  1. Changed1 schema field changedv1.3.1
    • 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"
      +}
  2. Changed3 schema fields changedv1.1.1
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / requests / items / additionalProperties
      Added value: +{}
  3. First observedv1.0.1

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by warning that responses can be LARGE and recommending batch-size limits. However, it does not disclose response format, error behavior, or rate limits, so it stays at a moderate transparency level.

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 appropriately short: a clear purpose statement followed by a focused warning. The core action is front-loaded, and every sentence earns its place without repeating schema or annotation content. This is exemplary conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description leaves the batch response shape unspecified and does not explain how partial failures or errors across multiple reports behave. It covers the main invocation constraints—batching, limits, and token safety—so an agent can call it, but it is not fully complete for an unfamiliar agent.

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 beyond the schema by advising that each batch should contain 2-5 reports and that each report should have small limits to avoid excessive data. This is genuinely useful parameter-level guidance that the input schema does not provide.

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: "Run multiple reports in a single request." This clearly distinguishes the tool from single-report siblings like ga_run_report, ga_run_realtime_report, ga_run_pivot_report, and ga_run_funnel_report. The batch nature is explicit and leaves no ambiguity about what the tool does.

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 the primary use case—batching multiple report requests—but it does not explicitly contrast with ga_run_report for single reports or other report variants. The token optimization warning provides practical guidance (limit to 2-5 reports, keep limits small), but there is no explicit when/when-not or alternative-selection guidance.

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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