athena_evidence_dependence_observe

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

No annotations are provided, and the description does not disclose side effects, persistence, idempotency, or any requirements. It only states the action and purpose, leaving the agent without knowledge of what happens when the tool is invoked.

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 one-sentence description is concise and front-loaded, with no redundant words. It earns its place but is under-specified.

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 tool has 5 parameters, nested objects, and no output schema or annotations, the description is far too minimal. It provides no guidance on parameter values, return behavior, or ordering, making it incomplete for effective use.

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 0%; the description does not mention any of the five parameters (scope, features, label, weight, evidence_ref) or explain their meaning. The schema alone is insufficient for correct invocation.

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 a specific verb ('record') and resource ('externally labelled evidence-dependence example'), and states the purpose ('for later empirical calibration'). This clearly distinguishes it from sibling tools like predict/fit/interval.

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 for recording labelled examples for calibration, which provides clear context but does not explicitly state when not to use it or compare with alternatives like athena_evidence_dependence_predict.

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