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

Annotations already establish the tool as read-only and non-destructive, so the safety profile is covered. The description adds useful behavioral context by explaining the return shape: aggregate totals plus individual rows per function and date, which matters because no output schema exists.

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 three short, front-loaded lines: purpose, usage cases, and return shape. Every sentence earns its place and there is no redundant phrasing.

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 two-parameter read-only tool with full schema coverage, the description covers purpose, usage, and return structure. It lacks detail on pagination or output field granularity, but no output schema exists and the core invocation context is present.

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 both parameters are already documented. The description adds no syntax, default, range, or scoping detail beyond what the schema provides, making the baseline 3 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?

States a specific verb, resource, and scope: it obtains AI token usage statistics for the workspace by date and function. 'Tokens de IA' distinguishes it from related usage tools such as get_storage_usage, so an agent can identify its purpose without opening the schema.

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 'Usar para' section gives clear use cases: seeing token consumption, auditing AI costs, and monitoring plan usage. It does not name alternative sibling tools or explicit when-not conditions, so it falls short of full routing guidance.

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