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

Annotations already declare readOnlyHint, destructiveHint false and openWorldHint false, so safety is covered. The description adds value by disclosing the return payload (calibration curve plus Brier score), but omits prerequisites such as labels needing to be binary and scores needing identical length, and says nothing about binning defaults.

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 pipe-delimited layout is front-loaded: trigger first, then aliases, then return values, with no prose padding. Minor redundancy in that the title is restated verbatim as the first token of the description.

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 three-parameter read-only statistics tool with full schema coverage, the description supplies the trigger, the outputs, and search aliases, which is adequate. The remaining gap is input preconditions (binary labels, equal-length arrays) that no other structured field covers.

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 already explains labels, scores, and n_bins, and the description adds no parameter-level meaning on top. It does not clarify that scores must be probabilities in 0-1 range or that n_bins is optional with 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 resource (calibration curve) and a concrete output differentiator (Brier score) plus the verb 'assess calibration of a risk model', which separates it from roc_auc and classification_metrics. It does not explicitly name the sibling tools it competes with, so it stops short of a 5.

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 'USE WHEN: assess calibration of a risk model' clause gives an explicit trigger condition, which is clearer than most sibling definitions. However, it offers no when-not guidance and does not route the agent to alternatives such as roc_auc or classification_metrics for discrimination-vs-calibration questions.

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