athena_bayes_predict

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does disclose that posterior/intervals remain model-conditional, which is a useful caveat, but it does not state whether the operation mutates state, requires permissions, or what the return format is.

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 wasted words. It is efficient but so sparse that it sacrifices informational value.

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?

Without annotations or an output schema, and with cryptic parameter names and nested objects, the description is far too sparse to enable correct invocation. It lacks any explanation of inputs, outputs, side effects, or usage context.

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 makes no mention of any of the six parameters. It fails to explain what features, regime, arm_id, ridge, scope, or target_coverage mean, leaving the agent without guidance for constructing valid inputs.

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 the tool performs Bayesian contextual prediction with full covariance and empirical interval calibration, clearly indicating it as a prediction tool. It distinguishes itself via the 'full-covariance' and 'empirical interval calibration' qualifiers, though it does not explicitly compare to siblings.

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 description implies usage when full-covariance Bayesian contextual prediction with calibrated intervals is needed, but it provides no explicit when/when-not guidance or alternatives. The technical qualifiers offer some inference, but no direct comparison to sibling tools like athena_transition_predict.

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