aetre_calibrate_scorer

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It transparently reveals the operation (fit a scaling model) and the precise return values, but it does not address side effects, whether this is a read-only analytics computation, or any execution cost relative to the optimization iterations it accepts.

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?

A single front-loaded sentence conveys the action, inputs, algorithm, and all four outputs without wasted words. Every element earns its place, and the most discriminant detail (Platt logistic scaling) leads the sentence.

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 fit-and-return tool, the description is nearly complete: it covers the algorithm, the expected inputs, and the four return values, which partially substitutes for the missing output schema. The only gap is the absence of usage guidance against sibling calibration and benchmarking tools.

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 documents every parameter (required scores/labels, optional api_key, iterations, learning_rate). The description only re-states that scores are continuous and labels are binary, adding little meaning beyond the schema's own wording. Baseline 3 applies.

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 states a specific verb ('fits'), a well-defined resource (Platt logistic scaling on continuous scores and binary labels), and enumerates the outputs (slope, intercept, ECE, Brier score). The calibration focus and named algorithm set it apart from the sibling tools, which cover VOI, staking, queues, and triage.

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?

There is no guidance on when to use this tool versus alternatives. None of the siblings (e.g., aetre_recall_scaling_curve, aetre_heldout_backtest) are referenced, and no exclusions or selection criteria are given. An agent must infer calibration context on its own.

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