classify

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds key behavioral context by specifying that the output is a probability distribution over a closed, unordered label set, which goes beyond the annotations and clarifies the tool's core semantics.

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, front-loaded with the core use case and followed by explicit exclusions. There is zero redundant text; every phrase earns its place. It is optimally structured for quick agent comprehension.

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?

Given the presence of an output schema (not shown but indicated) and annotations covering safety, the description covers the essential behavioral contract. It states the purpose, the label-set nature, and the probability output. It does not elaborate on parameter semantics, but that gap is partially mitigated by the schema. The overall picture is sufficient for correct invocation.

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 carries full responsibility for explaining parameters. It only implicitly references the 'labels' parameter as a closed set, but does not explain 'state', 'instructions', or the optional 'model'. The schema itself provides some clarity, but the description adds minimal parameter-level meaning.

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 explicitly states the tool's purpose: to return exactly one label from a closed, unordered set along with its probability distribution. It also distinguishes itself from sibling tools by specifying what it is not for (ordered levels, next actions, claims). This provides a precise verb-resource pairing and clear 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 includes both positive and negative usage guidance. It states when to use ('USE WHEN you need exactly one label...') and explicitly lists alternatives for cases not suited to this tool (score for ordered levels, route for next actions, verify for claims). This leaves no ambiguity about tool selection.

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