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Apply Recommendation

apply_recommendation
Destructive

Applies or dismisses Google recommendations. resource_names: comma-separated values from list_recommendations. action: apply | dismiss. apply can make costly changes such as budget increases (subject to the same budget guardrails as set_campaign_budget). confirm=false → dry-run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionNoapply
confirmNo
customer_idYes
resource_namesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, but the description adds the crucial facts beyond them: apply can trigger costly changes such as budget increases, those changes fall under the same guardrails as set_campaign_budget, and confirm=false produces a dry run. The dry-run default and cost warning are exactly the behavioral context an agent needs before a destructive write.

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?

Four terse fragments, each carrying a distinct piece of operational meaning (verb pair, parameter provenance, action values, cost/dry-run semantics). Nothing is padding and the riskiest information is front-loaded.

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?

With an output schema present, return values need no explanation, and the description covers the write risk, the dry-run path, and parameter sourcing. It stops short of saying whether applied changes can be rolled back or how partial failures across multiple resource_names are handled.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate, and it does for the two risky parameters: resource_names is described as comma-separated values sourced from list_recommendations, action is constrained to apply|dismiss, and confirm is explained as a dry-run switch. Only customer_id is left unstated, which is a minor gap given how conventional it is.

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

Purpose4/5

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

States a specific verb pair and resource ('Applies or dismisses Google recommendations'), which is far better than the title alone. The only ambiguity is the overlap with the sibling dismiss_recommendations, which the description never addresses, so an agent must guess which entry point to use for dismissal.

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?

It routes the agent to list_recommendations as the source of resource_names and gives the action enum values, which is concrete usage guidance. It does not say when to prefer this tool over dismiss_recommendations or list_recommendations, leaving that routing to inference.

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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