athena_uncertainty_calibrate

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

There are no annotations, so the description must disclose side effects and operational behavior. It states that the tool 'Return[s]' outputs, implying a read-only computation, but never explicitly says it does not modify state, requires prior data, or how calibration is performed.

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?

Single sentence, front-loaded with the action and output; no filler or 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?

The tool has 4 optional parameters, no output schema, and no annotations, yet the description provides no usage context, parameter semantics, or behavioral detail. It is a minimal one-liner that leaves key invocation questions unanswered.

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?

With 0% schema description coverage and no parameter descriptions, the description must explain the parameters. It only hints at target_coverage via 'coverage' and 'Bayesian intervals', but scope, arm_id, and regime are never explained.

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 uses the specific verb 'Return' and identifies the exact outputs: empirical coverage/error/width and a reliability-shrunk sigma correction for Bayesian intervals. This clearly distinguishes it from unrelated sibling tools, though it does not explicitly differentiate from similar calibration tools like athena_rgo_calibrate.

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?

No guidance is provided about when to use this tool versus other calibration or Bayesian interval tools. There are no exclusions, prerequisites, or references to alternatives.

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