athena_evidence_dependence_interval

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

With no annotations, the description must disclose behavioral traits. It only says 'Return', giving no detail on side effects, preconditions like an existing fitted model, error handling, or whether the operation is read-only. This is insufficient for safe agent invocation.

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 two short sentences with no redundancy. It is front-loaded with the core action and ends with a clarifying tag, making it efficient and readable.

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 four parameters, a nested object, no output schema, and no annotations, this description is far too sparse. It omits parameter meanings, expected return structure, and any usage context, leaving major gaps for the agent.

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 0%, and the description mentions none of the four parameters (l2, scope, features, confidence_z). The agent receives no meaning beyond the raw schema field names, making correct parameter construction guesswork.

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 returns a Laplace/Hessian logit interval for a fitted V10 evidence-dependence probability. The action verb 'Return' plus the resource and method specificity distinguish it from sibling tools like probability, predict, and fit.

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 explicit when-to-use or alternatives are provided. The phrase 'Model-conditional diagnostic' vaguely implies use after model fitting, but there is no guidance on prerequisites, exclusions, or why this tool over the many sibling evidence-dependence tools.

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