google_ads_recommendations_apply

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It explicitly states the tool is 'Mutating — the underlying change ... is committed to the campaign immediately and is NOT reversible through this tool.' It also discloses server-side re-validation to prevent injection and notes broader side effects on budget/device/schedule. This is exemplary transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each earning its place: purpose, return value, mutation/irreversibility warning, and validation plus pointer to the list tool. It is front-loaded with the core action and includes no filler or redundancy. This is an efficiently structured description.

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

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

For a small 2-parameter tool with no output schema, the description covers all necessary context: input, return value, mutation side effects, irreversibility, validation, and related tool. It gives the agent enough to know when and how to invoke it, and what to expect. No gaps remain.

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

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

The schema already documents both parameters with 100% coverage, including the resource_name pattern and customer_id fallback behavior. The description adds little beyond the schema: it reiterates the resource_name format and mentions server-side re-validation, which is more of a behavioral detail than parameter semantics. Therefore, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and object: 'Apply one Google Ads recommendation by resource name.' It clearly identifies the action and resource, and distinguishes itself from the sibling tool google_ads_recommendations_list by explicitly mentioning that tool for listing candidates. This is a clear, non-tautological purpose statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance by stating 'To list candidates use google_ads_recommendations_list,' establishing the typical workflow and alternative. It also warns that the action is irreversible and that some recommendation types change budget/device/schedule settings, giving the agent clear context for when to apply caution. This is strong conditional usage guidance.

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