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Python
Hybrid

google_ads_keywords_cross_adgroup_duplicates

Detect identical text-and-match-type keywords appearing in multiple ad groups, compare spend, conversions, and quality score, and get a consolidation recommendation to pause duplicates before restructuring.

Instructions

Finds the same text+match_type keyword appearing across multiple ad groups in a campaign. Returns groups of duplicate criteria with per-ad-group spend, conversions, and quality score, plus a consolidation recommendation (which copy to keep, which to pause/remove). Read-only. Duplicates compete in the auction and hurt aggregate quality score — run this before a keyword restructuring sprint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoAnalysis window used to compute per-copy spend and conversions. Default 'LAST_30_DAYS'. 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.
campaign_idYesCampaign to scan for duplicates.
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.

  1. Changed2 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: -"Analysis window used to compute per-copy spend and conversions. Accepts 'LAST_7_DAYS', 'LAST_14_DAYS', 'LAST_30_DAYS' (default), or 'YYYY-MM-DD..YYYY-MM-DD'."New value: +"Analysis window used to compute per-copy spend and conversions. Default 'LAST_30_DAYS'. 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."
  2. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv0.10.11
  4. Removedv0.10.9
  5. Addedv0.9.12
  6. Removedv0.9.6
  7. Addedv0.9.2
  8. Removedv0.9.1
  9. Addedv0.2.0
  10. Removedv0.3.1
  11. Addedv0.3.2
  12. Removedv1.0.6
  13. Addedv1.0.5

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It explicitly declares 'Read-only' and describes the return content, including per-ad-group spend, conversions, quality score, and a keep/pause recommendation. It does not cover every operational detail like pagination or empty-result behavior, but it clearly signals no side effects.

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 compact and front-loaded: it states the purpose, the output, the read-only behavior, and the recommended use context in just a few sentences. Every sentence adds value and there is no filler.

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?

Given the rich parameter schema and the description's clear output summary, the tool is fully understandable for selection and invocation. It defines the duplicate criterion, describes the returned metrics and recommendation, and provides when-to-run context, which is sufficient even without an output schema.

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 100%, and the schema already documents campaign_id, customer_id, and the period parameter in detail, including timezone nuances. The description adds no parameter-specific semantics beyond what the schema provides, so the baseline 3 is appropriate.

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 names a specific verb and resource: it finds the same text+match_type keyword appearing across multiple ad groups in a campaign, and it clearly states what is returned (duplicate groups with metrics and a consolidation recommendation). This differentiates it from keyword list, audit, and pause siblings.

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?

It gives an explicit use case: run before a keyword restructuring sprint, and explains why by noting that duplicates compete in the auction and hurt aggregate quality score. It does not explicitly name alternatives or when-not-to-use, so it stops short of a 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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