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Python
Hybrid

google_ads_negative_keywords_suggest

Analyze recent search-term performance to identify keywords wasting spend above a target CPA, and get suggested negative keywords with rationale. Read-only suggestions for review before applying.

Instructions

Analyses recent search-term performance and returns suggested negative keywords that waste spend relative to a target CPA. Returns candidates with text, suggested match_type, spend, conversions, and rationale (e.g. 'spend > 3x target CPA, 0 conversions'). Read-only — suggestions are not applied. Use google_ads_negative_keywords_add / add_to_ad_group to materialize the ones you want after operator review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoAnalysis window for the search-term sample. Default 'LAST_30_DAYS'. This tool also reads the equal-length window immediately before the one you request, so only fixed-length windows are accepted — calendar constants such as THIS_MONTH are rejected rather than silently replaced. 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.
target_cpaNoTarget CPA in the account's currency. Search terms whose effective CPA exceeds this are flagged. If omitted, the campaign's configured target_cpa is used when available.
ad_group_idNoRestrict analysis to a single ad group. Omit to analyse the whole campaign.
campaign_idYesCampaign whose search terms are analysed.
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": [
+      "LAST_7_DAYS",
+      "LAST_14_DAYS",
+      "LAST_30_DAYS",
+      "LAST_90_DAYS"
+    ],
+    "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. Accepts Google Ads predefined ranges ('LAST_7_DAYS', 'LAST_14_DAYS', 'LAST_30_DAYS' — default 'LAST_30_DAYS') or explicit 'YYYY-MM-DD..YYYY-MM-DD'."New value: +"Analysis window for the search-term sample. Default 'LAST_30_DAYS'. This tool also reads the equal-length window immediately before the one you request, so only fixed-length windows are accepted — calendar constants such as THIS_MONTH are rejected rather than silently replaced. 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. Addedv1.0.5

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden of behavioral disclosure. It explicitly states the tool is read-only and that suggestions are not applied, which is the critical side-effect information. It also describes the output surface (text, match_type, spend, conversions, rationale), though it does not go into details like data freshness, pagination, or ordering.

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 about four sentences and each sentence earns its keep: purpose/output, output fields, read-only behavior, and next-step tool routing. It is front-loaded with the core function and does not repeat schema details already present in the input 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?

Despite having no output schema and no annotations, the description supplies enough for an agent to decide when to call it, what to expect back, that it is non-mutating, and what to do next. Combined with the exhaustive parameter schema, no critical operational gap remains.

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 each parameter is already well documented. The description adds useful framing for target_cpa (search terms whose effective CPA exceeds it are flagged) and for the output rationale, but it does not substantially increase parameter-level understanding beyond the schema.

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 opens with a specific verb and resource: it 'Analyses recent search-term performance and returns suggested negative keywords that waste spend relative to a target CPA.' It clearly distinguishes the tool from siblings by stating that suggestions are not applied and by naming the add/add_to_ad_group tools as the materialization step.

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?

The description explicitly says the tool is read-only and that suggestions are not applied, and it names google_ads_negative_keywords_add / add_to_ad_group as the follow-up tools to use 'after operator review.' This gives an agent clear routing and workflow context without needing to inspect sibling schemas.

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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