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Python
Hybrid

google_ads_ad_performance_report

Report per-ad Google Ads performance: impressions, clicks, cost, conversions. Filter by ad group or campaign to diagnose ad-level results.

Instructions

Report per-ad performance across Google Ads ad_group_ad rows. Returns one row per ad shaped as {ad_id, ad_type, status ('ENABLED'|'PAUSED'|'REMOVED'), ad_group_id, ad_group_name, campaign_id, campaign_name, metrics} where metrics contains impressions, clicks, cost_micros, cost (currency), conversions, ctr, average_cpc_micros, average_cpc, cost_per_conversion_micros, cost_per_conversion. Filterable by ad_group_id and/or campaign_id (both optional, both numeric). Read-only; no mutation. For ENABLED-only A/B comparison within a single ad group with WINNER/LOSER verdicts use google_ads_ad_performance_compare; for campaign-level aggregates use google_ads_performance_report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoReporting window for the metrics. Default 'LAST_30_DAYS'. Use a shorter window (LAST_7_DAYS / LAST_14_DAYS) when diagnosing recent changes; use LAST_90_DAYS for trend baselines. Also accepts an explicit range in GAQL spelling — "BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'", both endpoints inclusive, in the account's time zone — for a window no trailing constant can reach (e.g. a single past calendar month). One asymmetry to know about: every constant except LAST_90_DAYS is resolved by Google Ads in the account's reporting time zone, whereas LAST_90_DAYS has no API constant and is expanded by mureo into the 90 days ending yesterday **on the server's date**, so its edges can differ by a day when the server and the account are in different zones. Pass an explicit range when the exact boundary matters.
ad_group_idNoOptional ad group ID as a numeric string (e.g. '145680123456') to restrict results to a single ad group. Omit to include every ad group matching the campaign filter.
campaign_idNoOptional campaign ID as a numeric string (e.g. '23743184133') to restrict the report to a single campaign. Omit to aggregate across every campaign in the account.
customer_idNoGoogle Ads customer ID as a 10-digit string without dashes (e.g. '1234567890'). Optional — falls back to GOOGLE_ADS_CUSTOMER_ID / GOOGLE_ADS_LOGIN_CUSTOMER_ID from the configured credentials when omitted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changedv0.17.1
    • addedInput schema / properties / period / anyOf
      Added value: +[
+  {
+    "enum": [
+      "TODAY",
+      "YESTERDAY",
+      "THIS_WEEK_SUN_TODAY",
+      "THIS_WEEK_MON_TODAY",
+      "LAST_BUSINESS_WEEK",
+      "LAST_WEEK_SUN_SAT",
+      "LAST_WEEK_MON_SUN",
+      "LAST_7_DAYS",
+      "LAST_14_DAYS",
+      "LAST_30_DAYS",
+      "LAST_90_DAYS",
+      "THIS_MONTH",
+      "LAST_MONTH"
+    ],
+    "type": "string"
+  },
+  {
+    "pattern": "\\ABETWEEN '([0-9]{4}-[0-9]{2}-[0-9]{2})' AND '([0-9]{4}-[0-9]{2}-[0-9]{2})'\\Z",
+    "type": "string"
+  }
+]
    • changedInput schema / properties / period / description
      Previous value: -"Reporting window for the metrics. Default 'LAST_30_DAYS'. Use a shorter window (LAST_7_DAYS / LAST_14_DAYS) when diagnosing recent changes; use LAST_90_DAYS for trend baselines."New value: +"Reporting window for the metrics. Default 'LAST_30_DAYS'. Use a shorter window (LAST_7_DAYS / LAST_14_DAYS) when diagnosing recent changes; use LAST_90_DAYS for trend baselines. Also accepts an explicit range in GAQL spelling — \"BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'\", both endpoints inclusive, in the account's time zone — for a window no trailing constant can reach (e.g. a single past calendar month). One asymmetry to know about: every constant except LAST_90_DAYS is resolved by Google Ads in the account's reporting time zone, whereas LAST_90_DAYS has no API constant and is expanded by mureo into the 90 days ending yesterday **on the server's date**, so its edges can differ by a day when the server and the account are in different zones. Pass an explicit range when the exact boundary matters."
    • removedInput schema / properties / period / enum
      Removed value: -[
-  "TODAY",
-  "YESTERDAY",
-  "LAST_7_DAYS",
-  "LAST_14_DAYS",
-  "LAST_30_DAYS",
-  "LAST_90_DAYS",
-  "THIS_MONTH",
-  "LAST_MONTH"
-]
  2. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv0.9.12
  4. Removedv0.9.6
  5. Addedv0.9.2
  6. Removedv0.9.1
  7. Addedv1.0.5

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries behavioral disclosure burden; it states 'Read-only; no mutation', gives the exact row shape and metric names, and discloses the LAST_90_DAYS server-date timzone asymmetry. This is detailed and goes well beyond a generic statement.

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 front-loaded with purpose and return shape, then filters and alternatives. The long period explanation is dense but every sentence earns its place; there is no fluff or repetition.

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 and no annotations, the description provides return shape, metrics list, filter semantics, read-only guarantee, and alternative routing. An agent has enough information to select and invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description adds meaning beyond the schema: the default period, gaql range syntax, timezone edge-case, filter scoping semantics with 'Omit to include every...' phrases, and customer_id fallback behavior. This adds substantial value.

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: 'Report per-ad performance across Google Ads ad_group_ad rows' and specifies the exact return shape. It also distinguishes itself from siblings by naming google_ads_ad_performance_compare for A/B comparison and google_ads_performance_report for campaign-level aggregates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use this tool versus alternatives: 'For ENABLED-only A/B comparison within a single ad group with WINNER/LOSER verdicts use google_ads_ad_performance_compare; for campaign-level aggregates use google_ads_performance_report.' It also advises period selection, e.g. shorter windows for recent changes and LAST_90_DAYS for trend baselines.

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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