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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds one piece of real value, that a deterministic algorithm engine computes the result rather than an LLM, but says nothing about input validation, error behavior, or how bins are chosen.

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?

Very short and front-loaded: purpose first, then return values, then the engine note. The bracketed 【校准曲线】 prefix duplicates the annotation title and is slightly redundant, but nothing else is padded.

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 naming the calibration curve and Brier score in the description is genuinely useful. However, for a tool whose inputs are counterintuitively string-typed arrays, the description leaves input format and length-matching expectations unaddressed, which is a meaningful gap.

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 100%, so the schema carries most of the parameter burden and the baseline is 3. The description adds no clarification of the notable ambiguity, namely that labels and scores are typed as strings while scores are described as probabilities in 0-1, nor whether n_bins has a default.

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 verb and resource: evaluate a prediction model's calibration and draw a calibration curve, plus the returned artifacts (curve and Brier score). It does not distinguish itself from the nearest sibling act_roc, so an agent must infer the calibration-vs-discrimination split on its own.

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 'when the user wants to assess calibration / draw a calibration curve' framing gives an implicit trigger, but there is no explicit when-not and no mention that act_roc is the discrimination counterpart. Nothing is said about prerequisites or which inputs are required versus optional.

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