athena_evidence_dependence_predict

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to mention what happens if no fitted model exists, the output format or structure, or whether there are any side effects. The description is too terse to convey important behavioral aspects for a prediction tool.

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 a single, concise sentence that is front-loaded with the core action. It contains no filler or redundant wording, making it efficient and easy to parse.

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 complex nested parameters, no output schema, and no annotations. The one-line description fails to explain critical aspects such as the format of 'features', the meaning of 'scope', or what the prediction output looks like. This is inadequate for an agent to use the tool confidently, even compared to similar tools in the same family.

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?

The schema has 0% description coverage, and the description provides no information about the 'scope' or 'features' parameters. The nested 'features' object is especially ambiguous, leaving the agent without any guidance on the expected structure or content. The description does not compensate for the schema's lack of detail.

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: 'Predict pairwise evidence dependence under a previously fitted scoped calibration model.' It uses a specific verb ('predict') and resource ('pairwise evidence dependence'), and the prerequisite of a fitted model distinguishes it from sibling tools like athena_evidence_dependence_fit or athena_evidence_dependence_observe.

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 phrase 'under a previously fitted scoped calibration model' implies that the tool should be used after a fitting step, providing clear context for when it is applicable. However, it does not explicitly mention alternatives or when not to use it, such as when an interval or probability estimate is needed.

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