classify

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

No annotations are provided, so the description carries the burden. It discloses the tool is paid ($0.0003 KAS) and that results are constrained to the label set. However, it does not cover authentication, rate limits, or other behavioral traits like speed or data handling.

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 a single sentence, front-loading the core action and key constraints. It includes cost information efficiently. Slightly more structured formatting could help, but it is concise and clear.

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 simple classification tool with no output schema, the description covers the main purpose and key parameters. However, it lacks details on the return format (e.g., single string or array) and any limitations (e.g., maximum text length). This is adequate but not comprehensive.

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?

Although schema coverage is 0%, the description adds value by clarifying that 'multi=true' enables multiple labels and that labels must be between 2 and 32 items. This goes beyond the schema which only provides types and defaults.

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 the tool classifies text into labels, with a constraint to the provided label set. It specifies input range (2-32 labels) and optional multi-label. This distinguishes it from sibling tools like 'extract' or 'summarize' which have different purposes.

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 implies when to use (for text classification) but does not explicitly mention when not to use or provide alternatives among siblings. No exclusions or context for choosing over other tools.

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