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

Beyond the read-only/open-world annotations, the description discloses pricing, billing on empty series, the monthly row shape, and the history_id linkage. These are behavioral facts an agent needs to predict side effects and costs, and nothing contradicts the annotations.

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?

Three short sentences cover function, data availability, cost, return shape, and the integration workflow. There is no filler, and the most important behavior is front-loaded.

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 no-output-schema tool, the description does state the output shape ('one row per month') and the two metrics from the first sentence. A slightly fuller return-field listing would make it fully self-sufficient, but combined with the 100%-coverage schema it is nearly complete.

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 description coverage is 100%, so the schema already documents all four parameters. The description adds no new parameter-level meaning beyond restating that history_id comes from ai_overview, which is already in the schema, so 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 identifies a specific resource ('AI mentions and AI search volume for a domain') and a clear temporal aggregation ('month-by-month'), so an agent knows what this tool produces. It references ai_overview as the source of history_id, but it never explicitly contrasts itself with sibling tools such as ai_check or ai_overview, so it falls just short of full sibling differentiation.

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 gives clear operational context: data begins August 2025, the call costs 4 credits even on an empty result, and the history_id workflow attaches the trend to an ai_overview. It does not explicitly say when to prefer this tool over alternatives, however, so it stops short of a 5.

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