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google-ecommerce-mcp

by MoonEyes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Google Ads performance

gads_performance
Read-onlyIdempotent

Query Google Ads spend, impressions, clicks, conversions, conversion value, CTR, CPC, CPA, and ROAS by account, campaign, or ad group over a date range, optionally split by date, device, or network.

Instructions

Google Ads performance by account, campaign or ad group over a date range: spend, impressions, clicks, conversions, conversion value, CTR, CPC, CPA and ROAS, optionally split by date, device or network. Built from fixed aggregated GAQL templates (searchStream), highest spend first. Returns "rows", "totals" (ROAS = sum of values / sum of spends) with "totals_complete". start_date and end_date are required (empty -> invalid_dates), the "currency" (looked up when no row), a "date_range" in the account timezone with "data_complete", the unit and definition of each metric, and the "attribution" used.

Input Schema

NameRequiredDescriptionDefault
levelNoRow level: account, campaign or ad_groupcampaign
limitNoMaximum rows returned (1 to 1000), highest spend first; totals cover up to 10,000 rows (see totals_complete)
segmentNoOptional split of each row: none, date, device or networknone
end_dateNoRequired: last day, YYYY-MM-DD (account timezone)
client_idNoClient id from clients_list; empty uses the only client
start_dateNoRequired: first day, YYYY-MM-DD (account timezone)
customer_idNoGoogle Ads customer id (digits, dashes allowed), as listed by gads_accounts; empty uses the client's (or GADS_CUSTOMER_ID)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.5.0
    • addedInput schema / properties / client_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Client id from clients_list; empty uses the only client",
+  "title": "Client Id"
+}
    • changedInput schema / properties / customer_id / description
      Previous value: -"Google Ads customer id (digits, dashes allowed), as listed by gads_accounts; empty uses the configured GADS_CUSTOMER_ID"New value: +"Google Ads customer id (digits, dashes allowed), as listed by gads_accounts; empty uses the client's (or GADS_CUSTOMER_ID)"
  2. Addedv0.4.0

TDQS

A4.1/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent/openWorld annotations: it discloses the fixed-template query model, highest-spend-first ordering, totals semantics (ROAS = sum of values / sum of spends), the totals_complete caveat that totals cover up to 10,000 rows, the invalid_dates error case, currency lookup when no row is present, and the account-timezone date_range with data_complete. That is unusually rich behavioral disclosure.

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?

Front-loaded with the core purpose and metric list, then packs return-shape details into a dense second half. Every sentence carries information, though the long comma-spliced tail listing returned fields is harder to scan than the opening.

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 carries full responsibility for explaining returns, and it does: rows, totals, totals_complete, currency, date_range, data_complete, metric units/definitions and attribution. An agent can call this correctly and interpret the output without further documentation.

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 coverage is 100% so the baseline is 3, but the description earns a bump by surfacing the hidden requirement that start_date/end_date must be non-empty (they are schema-optional with default ''), the invalid_dates failure mode, and the currency/customer_id fallback behavior. It also documents the shape of the returned rows and totals that the parameters drive.

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 — 'Google Ads performance by account, campaign or ad group over a date range' — plus the exact metric set and segmentation options. It also implies differentiation from gads_query by noting results come from 'fixed aggregated GAQL templates (searchStream),' though it never names that sibling explicitly.

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?

Usage is implied by the metric/level/date-range framing, and it flags that start_date and end_date are required (empty -> invalid_dates). But it offers no explicit when-to-use vs when-to-use-something-else guidance, and never routes the agent to gads_query for arbitrary GAQL or to gads_accounts for account discovery beyond a parenthetical.

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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