google_ads_set_attribution_model

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

The description discloses the main action but lacks details on side effects (e.g., immediate application, overwriting behavior), permissions required, or idempotency. Since no annotations are provided, the description should cover these traits more thoroughly.

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 well-structured with Args, Returns, Example, and models sections. It is slightly verbose but front-loads the main purpose and uses bullet points for readability. Every sentence adds value.

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 output schema exists, the description adequately covers parameters and usage. It provides an example and model details. However, it could include error handling or prerequisite steps (e.g., conversion action must exist) for full completeness.

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 description adds significant meaning beyond the input schema by explaining each parameter (customer_id, conversion_action_id, attribution_model) and listing possible values for attribution_model with descriptions. This compensates for the 0% schema description coverage.

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 it sets the attribution model for a conversion action with a specific verb and resource. It provides a concise purpose and explains what attribution models do, distinguishing it from sibling tools that perform other actions.

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 includes a recommendation to use DATA_DRIVEN with sufficient data and lists model options. However, it does not explicitly state when to use this tool versus alternatives, such as when to modify vs. create conversion actions. The example and model list provide good context but lack exclusions.

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