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google-ecommerce-mcp

by MoonEyes
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Python
Remote

GA4 report

ga4_report
Read-onlyIdempotent

Run GA4 data reports to break down traffic, conversions or revenue by dimensions over a date range, returning top rows, totals and metric definitions.

Instructions

Run a Google Analytics 4 report (Data API runReport): traffic, conversions or revenue split by any dimensions over a date range. Returns the top rows ({"rows": [{dimension: value, metric: number}]}), "totals" over every row, "row_count" and "truncated", the unit and definition of each metric under "metrics", the currency, and a "date_range" resolved to calendar dates in the property's timezone with "data_complete". Use ga4_realtime for the last 30 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows returned (1 to 1000); totals always cover every row
metricsNoGA4 API metric names, e.g. sessions, totalUsers, ecommercePurchases, purchaseRevenue, keyEvents
end_dateNoYYYY-MM-DD, NdaysAgo, yesterday or todayyesterday
dimensionsNoGA4 API dimension names, e.g. sessionDefaultChannelGroup, landingPage, sessionSource, yearMonth, itemName
start_dateNoYYYY-MM-DD, NdaysAgo, yesterday or today28daysAgo
property_idNoNumeric GA4 property id, as listed by ga4_properties; empty uses the configured GA4_PROPERTY_ID
channel_groupNoOptional exact filter on sessionDefaultChannelGroup, e.g. 'Organic Search'; empty for all channels

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.3.0
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum rows returned (1 to 1000)"New value: +"Maximum rows returned (1 to 1000); totals always cover every row"
    • addedInput schema / properties / property_id
      Added value: +{
+  "default": "",
+  "description": "Numeric GA4 property id, as listed by ga4_properties; empty uses the configured GA4_PROPERTY_ID",
+  "title": "Property Id",
+  "type": "string"
+}
  2. Changed8 schema fields changedv0.1.2
    • addedInput schema / properties / channel_group / description
      Added value: +"Optional exact filter on sessionDefaultChannelGroup, e.g. 'Organic Search'; empty for all channels"
    • addedInput schema / properties / dimensions / description
      Added value: +"GA4 API dimension names, e.g. sessionDefaultChannelGroup, landingPage, sessionSource, yearMonth, itemName"
    • addedInput schema / properties / end_date / description
      Added value: +"YYYY-MM-DD, NdaysAgo, yesterday or today"
    • addedInput schema / properties / limit / description
      Added value: +"Maximum rows returned (1 to 1000)"
    • addedInput schema / properties / limit / maximum
      Added value: +1000
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • addedInput schema / properties / metrics / description
      Added value: +"GA4 API metric names, e.g. sessions, totalUsers, ecommercePurchases, purchaseRevenue, keyEvents"
    • addedInput schema / properties / start_date / description
      Added value: +"YYYY-MM-DD, NdaysAgo, yesterday or today"
  3. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world, yet the description adds substantial context beyond them: the returned shape (rows, totals, row_count, truncated), the fact that totals cover every row regardless of limit, metric unit/definition metadata, currency, and a date_range resolved to calendar dates in the property's timezone with data_complete. This is exactly the behavioral disclosure structured fields cannot carry.

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?

Purpose and scope come first, followed by the return contract and the sibling pointer — front-loaded and information-dense with no filler. The return-shape clause packs several quoted keys into one sentence, which reads slightly clunky but is justified given there is no output schema.

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

Completeness5/5

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

With no output schema, the description must describe the response, and it does so thoroughly (rows, totals, row_count, truncated, metrics, currency, resolved date_range). For a 7-parameter read tool with full schema coverage and complete annotations, nothing an agent needs to call it correctly is missing.

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 100%, so the schema already documents limit, metrics, dimensions, dates, property_id and channel_group, including defaults and the 1-1000 bound. The description adds no syntax or format detail beyond that, so the baseline 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?

States a specific verb and resource ('Run a Google Analytics 4 report (Data API runReport)') and names the scope: traffic, conversions or revenue split by dimensions over a date range. It also explicitly contrasts with the ga4_realtime sibling, so an agent can tell the two apart without opening schemas.

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?

Explicitly routes the agent to ga4_realtime for the last 30 minutes, which is a clear when-to-use-this-vs-alternative statement. It lacks broader eligibility context (e.g. where metrics/dimensions account for unavailable combinations), so it falls short of a full 5.

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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