google_ads_recommendations_apply

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

With no annotations provided, the description carries the full burden. It clearly states that the change is mutating, committed immediately, and not reversible through this tool. It also mentions server-side validation and the potential for broader changes (budget, device, schedule). This provides good behavioral context, though it omits details like auth requirements or error handling.

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 extremely concise: three sentences that front-load the core purpose, immediately follow with critical behavioral transparency, and end with a useful sibling reference. Every sentence earns its place with no redundancy.

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?

Given the tool's simplicity (one required parameter, no output schema), the description fully addresses what the agent needs: it explains the action, the return value, the irreversibility, the validation, and the correct precursor tool for listing. No gaps remain for effective use.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that customer_id is optional with fallback to environment variables and that resource_name is re-validated against a strict regex, beyond what the schema provides. This enhances understanding of parameter behavior.

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 clearly states 'Apply one Google Ads recommendation by resource name' and distinguishes from the sibling google_ads_recommendations_list by noting its use for listing candidates. The verb 'apply' combined with the resource 'recommendation' makes the purpose specific and unambiguous.

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 explicitly directs users to google_ads_recommendations_list for listing candidates, providing a clear alternative. It also warns that some recommendation types affect budget, device, or schedule settings, but does not explicitly state when not to use the tool or provide exhaustive exclusions.

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