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

With no annotations, the description carries the full burden and does well: it discloses cost (0.005 USDC per call via x402), the single-label selection behavior, and the shape of the response (label choice, confidence, one-line reason). It stops short of stating rate limits, failure modes, or the 402 retry flow, which only appears in the paymentProof schema field.

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?

Two sentences, front-loaded with the capability, then the output shape, then pricing. Every clause carries information an agent needs and nothing is repeated from the schema.

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?

There is no output schema, so the description correctly compensates by describing the return values (best label, confidence, one-line reason). Combined with a 100%-covered input schema, an agent has enough to call it, with only pricing/retry mechanics left implicit across description and schema.

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 coverage is 100%, so text, labels, and paymentProof are already fully documented in the schema, including the two-call 402 payment handshake. The description only adds the semantic that 'labels' is a candidate set from which one winner is chosen, which is the baseline 3 when the schema does the heavy lifting.

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 technique (zero-shot classification) and the exact operation (picks the best of the provided labels for the text), plus what comes back (confidence and a one-line reason). It is clearly distinct from fixed-label siblings like ai_sentiment, though it never names a sibling explicitly.

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?

'Zero-shot' plus 'best of the provided labels' implies when to reach for it (custom label sets, single-label selection rather than multi-label tagging), but there is no explicit when-to-use framing and no comparison against ai_sentiment, ai_extract, or ai_answer.

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