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

Annotations already declare readOnlyHint=true, destructiveHint=false and closed-world, so the safety profile is covered. The description adds value beyond them by disclosing the return payload (AUC, optimal threshold, sensitivity/specificity) and by asserting that a real algorithm engine computes the result rather than an LLM, which matters for trust in a statistical output. It does not state minimum sample requirements or what happens with degenerate input (e.g. a single class present).

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?

It is compact and front-loaded: the bracketed subject leads, the usage condition follows, then the return values. The bracketed title partially repeats the annotation title, and the parenthetical about the algorithm engine sits awkwardly at the end, but nothing is padded or redundant at length.

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 two-parameter statistical tool with no output schema, the description covers what is needed: inputs required, what is computed, and what comes back. The main remaining hole is input formatting and alignment of the two arrays, plus whether an actual plot image is produced alongside the numeric metrics, which the phrase 'draw ROC' leaves ambiguous.

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 the schema already documents both parameters, making 3 the baseline. The description reinforces their meaning (gold-standard labels vs. prediction scores) but adds no format guidance, which is a real gap given the schema types both fields as arrays of strings without stating delimiters, ordering, or that labels and scores must be positionally aligned.

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 names a specific verb+resource pair (compute AUC / draw ROC curve) and states the required inputs (gold-standard labels plus prediction scores), so an agent can immediately tell this is a discrimination-performance tool. It does not name the closest siblings (act_delong for AUC comparison, act_calibration, act_classification_report), so differentiation still requires inference from the names alone.

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?

It gives a clear triggering condition: the user has gold-standard labels and predicted scores and wants AUC/ROC. That is enough context to select the tool for the common case. It stops short of exclusions or alternatives, e.g. that comparing two AUCs should route to act_delong or that probability calibration belongs to act_calibration.

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