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 that the tool returns calibrated confidence and a score for every label, which is useful behavioral information. However, it does not disclose whether the tool mutates any state, whether it requires authentication, or what happens with ties or invalid labels. The description adds some behavioral context but not comprehensive transparency.

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 two sentences with no filler. The core action and output are front-loaded, and the use cases are listed compactly. Every word earns its place.

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?

The tool has an output schema, so return values are already documented. The description covers the tool's purpose, output characteristics (calibrated confidence, per-label scores), and typical use cases. It lacks explicit guidance on the 'instructions' parameter and edge cases, but for a classification tool with an output schema, this is reasonably 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 0%, so the description must compensate. It explains the purpose of 'text' and 'labels' implicitly ('Pick the single best label for a text') and mentions 'instructions' indirectly through 'calibrated confidence' but does not explain the format or role of the 'instructions' parameter. The description adds meaning beyond the schema for the two required parameters but leaves the optional 'instructions' parameter under-specified.

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 states a specific verb ('Pick'), a specific resource ('the single best label for a text'), and a clear outcome ('with calibrated confidence and a score for every label'). It also names the intended use cases (routing, tagging, moderation, triage), which distinguishes it from generic classification tools and from siblings like 'decide' or 'score'.

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 explicitly lists use cases (routing, tagging, moderation, triage) and implies that this tool is for single-best-label selection, which differentiates it from 'score' (likely multi-label scoring) and 'decide' (likely binary or decision-oriented). It does not explicitly state when NOT to use it or name alternatives, but the use-case list and 'single best label' phrasing provide clear context.

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