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Python
Hybrid

google_ads_monitoring_cv_goal

Compares a campaign's 7-day daily conversion average to a target, flags healthy, warning, or critical status, and identifies the bottleneck—impressions, CTR, or CVR—so you know where to optimize next.

Instructions

Evaluate a Google Ads campaign's daily conversion rate against a target and identify the dominant bottleneck. Returns {campaign_id, target_cv_daily, current_cv_daily (7-day conversions / 7), performance_analysis (full google_ads_performance_analyze payload), deviation_pct, status ('healthy' when >= target, 'warning' when >= target0.8, 'critical' otherwise, 'unknown' when the performance report could not be retrieved, in which case current_cv_daily, deviation_pct and bottleneck are null), bottleneck ('impression'|'ctr'|'cvr'), issues:[strings], summary, suggested_workflow?}. Bottleneck routing: 'impression' when analyze insights mention impression drops or impressions<clicks10; 'ctr' when CTR<2%; 'cvr' otherwise. A part that could not be retrieved is null and named in issues, and nothing is derived from it. The evaluation window is hardcoded to LAST_7_DAYS. Read-only. For CPA-target evaluation use google_ads_monitoring_cpa_goal; for the underlying composite use google_ads_performance_analyze.

Input Schema

NameRequiredDescriptionDefault
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.
target_cv_dailyYesTarget daily conversion count (e.g. 5.0 means 5 conversions per day). Required. status 'warning' fires at 80-100% of target; 'critical' below 80%.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  2. Addedv0.9.12
  3. Removedv0.9.6
  4. Addedv0.9.2
  5. Removedv0.9.1
  6. Addedv1.0.5

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it does so: it discloses read-only status, hardcoded LAST_7_DAYS window, the exact status thresholds (healthy >= target, warning >= target*0.8, critical otherwise, unknown when retrieval fails), the null-propagation rule for missing report parts, and the bottleneck routing logic. These are behavioral facts the agent could not infer from the schema.

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?

The description is front-loaded with purpose, then return shape, then routing rules, then alternatives — a logical order. It is dense and long, but nearly every clause carries operational information; only the status-threshold restatement mildly duplicates the 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?

There is no output schema, so the return-shape enumeration (all keys, null semantics, enum values for bottleneck and status) is essential and fully provided. Combined with the routing thresholds and the fallback/read-only notes, the agent has everything needed to call and interpret this tool correctly.

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%, and the schema already documents campaign_id format, customer_id fallback behavior, and target_cv_daily units plus the 80%/100% thresholds. The description restates the status thresholds but adds nothing parameter-specific beyond what the schema already supplies, so the baseline 3 applies.

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: 'Evaluate a Google Ads campaign's daily conversion rate against a target and identify the dominant bottleneck.' This is clearly distinct from sibling tools, and the author explicitly names google_ads_monitoring_cpa_goal and google_ads_performance_analyze so the agent can route without inspecting schemas.

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 when-to-use-this vs when-to-use-alternatives rule at the end: 'For CPA-target evaluation use google_ads_monitoring_cpa_goal; for the underlying composite use google_ads_performance_analyze.' The evaluation window is also pinned to LAST_7_DAYS, removing any ambiguity about scope.

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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