score_calibration

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true, so the safety profile is clear. The description adds interpretive guidance ('Lower Brier/log score = better') and explains input alignment ('predictions[i]... outcomes[i]...'), which enriches behavioral understanding without contradicting annotations.

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 description is concise at ~100 words, front-loads the main purpose, and every sentence adds value. It includes purpose, usage, metric interpretation, input alignment, alternative tool, and a note about being free, all without redundancy.

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?

Given the presence of an output schema (not shown but indicated), the description does not need to explain return values. It covers purpose, usage, parameter alignment, and alternatives, providing complete context for an agent to use the tool correctly.

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 parameters are already documented. The description adds context by explaining the index alignment between predictions and outcomes, which is not in the schema. This adds some value but does not provide extensive additional semantics beyond the schema.

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 clearly states the tool's function: scoring calibration of probability predictions using Brier and log scores. It specifies the verb 'score' and the resource 'calibration', and distinguishes itself from the sibling 'score_convergence' by explicitly stating the alternative use case.

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?

Explicitly indicates when to use (evaluate forecaster accuracy, model calibration, prediction-market fairness) and when not to ('for comparing multiple forecasters' agreement, use score_convergence instead'). Provides clear direction for the agent.

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