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lucagalvani

google-ads-agent

by lucagalvani

policy_status

Read-only

Check current policy before making changes: see which actions are autonomous, propose-only, or forbidden, evidence thresholds, and remaining change budget.

Instructions

Show the active policy: which actions are autonomous, propose-only, or forbidden; the evidence thresholds each autonomous action must clear; and how much of the change budget is left. Call this before attempting any change so you know what will be permitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idNo
customer_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate that this is a safe, read-only query. The description adds useful context about what the policy covers and that it should be checked before mutations, but it does not disclose any behavioral traits beyond what annotations already provide.

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: the first sentence states the core purpose and output content, and the second gives an action-oriented usage instruction. Every sentence earns its place with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main purpose, output categories, and when to call the tool, while an output schema is present to handle return-value details. The only notable gap is the lack of explanation about the optional parameters, which prevents a perfect completeness score.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not mention campaign_id or customer_id at all. While the parameter names are somewhat self-explanatory and both are optional with defaults, the description fails to explain whether these parameters scope the policy to a specific campaign/customer or how they affect the returned policy.

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 states a specific verb ('Show') and resource ('the active policy'), then enumerates exactly what the tool reports: allowed action modes, evidence thresholds, and remaining change budget. This clearly distinguishes it from sibling tools that operate on campaigns, keywords, or proposals.

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?

The description gives explicit usage guidance: 'Call this before attempting any change so you know what will be permitted.' It does not name alternatives or exclusions, but the tool is unique among siblings as a policy-status read, so the when-to-use guidance is sufficient for a strong score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.