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

The description adds meaningful behavior beyond the annotations: it states that classification is deterministic and that pricing is $0.003 USDC per successful call, with failed calls uncharged. The annotations already cover safety traits, so this is a useful additional disclosure, though it does not describe input handling or return shape.

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 front-loaded with the purpose and taxonomy, followed by deterministic behavior and pricing. Every sentence carries relevant information, and the taxonomy list is necessary to define the classification labels.

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?

With no output schema and an empty input schema, the description should explain how the text is supplied and what the classification result looks like. It lists the taxonomy labels but omits both the input mechanism and the return format, leaving an agent without essential calling context.

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 tool has zero parameters, so the baseline for parameter semantics is 4. The description does not need to explain parameters, and it correctly focuses on taxonomy and cost instead.

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 gives a specific verb and resource: rule-based multi-label text classification over a fixed taxonomy, and enumerates every label. This clearly distinguishes it from sibling utilities such as extract, validate, or hash.

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 explains what classify does but gives no when-to-use guidance, no exclusions, and no alternatives among the siblings. An agent must infer that this tool should be selected for text classification rather than extraction or validation.

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