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mureo

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Python
Hybrid

google_ads_btob_optimizations

Identify B2B-specific Google Ads optimization opportunities by checking ad schedule coverage, device CPA disparity, and informational-query ratio. Get prioritized suggestions for campaign improvements.

Instructions

Run three B2B-specific optimization checks (ad schedule, device CPA disparity, informational-query ratio) against a Google Ads campaign. Returns {campaign_id, campaign_name, period, suggestion_count, suggestions:[{category ('schedule'|'device'|'search_terms'), priority ('HIGH'|'MEDIUM'|'LOW'), message}]}. Schedule fires HIGH when no ad schedule is set, MEDIUM for weekend delivery. Device fires MEDIUM when Mobile CPA > Desktop CPA * 1.3, LOW when Tablet has zero conversions with spend. Search-terms fires MEDIUM when informational patterns exceed 20% of queries. Read-only. Use this when the advertiser self-identifies as B2B. For general campaign diagnosis use google_ads_performance_analyze.

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.
campaign_idYesCampaign ID as a numeric string without dashes (e.g. '23743184133'). Obtain via google_ads_campaigns_list.
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

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full transparency burden. It fully discharges it by declaring the tool read-only, detailing every trigger threshold (e.g., Mobile CPA > Desktop CPA * 1.3, informational patterns > 20%), and specifying the exact output structure. This is far beyond what the input schema alone would convey.

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 densely packed but every sentence earns its place: purpose, return contract, trigger logic, read-only status, and routing to the alternative. It is front-loaded with the core action and avoids filler or repetition of schema content.

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?

Because there is no output schema, the description fully specifies the return value, including nested suggestion fields, categories, and priorities. The threshold logic for all three checks is also included, so an agent can invoke the tool and interpret its results without additional lookups. Nothing material 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 thoroughly documents campaign_id, customer_id, and the period parameter. The description itself does not add parameter-level semantics beyond referencing campaign_id in the return shape. Baseline 3 is appropriate because the schema does the heavy lifting.

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 uses a specific verb ('Run'), names the resource ('Google Ads campaign'), and enumerates exactly three checks: ad schedule, device CPA disparity, and informational-query ratio. It also states the return shape and explicitly names google_ads_performance_analyze as the general alternative, so it is clearly distinguishable from siblings.

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?

It gives an explicit condition for use: 'Use this when the advertiser self-identifies as B2B.' It also names the alternative for other cases: 'For general campaign diagnosis use google_ads_performance_analyze.' This is direct, actionable routing guidance with no ambiguity.

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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