athena_evidence_dependence_fit

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

With no annotations, the description carries the full burden of behavioral disclosure. 'Fit a model' implies a training operation, but it does not disclose whether the model is stored, returned, or if there are side effects, permissions, or state changes. It also does not describe the output format or any failure modes. This is a significant gap for a model-fitting 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, compact sentence that conveys the core purpose without extraneous words. Its structure is straightforward and front-loaded with the action. It could be expanded with parameter details, but as a concise summary it is effective.

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?

This tool has 3 parameters, no output schema, no annotations, and a description that only hints at one parameter (scope). The agent has no information about what the function returns, how to supply the 'labelled examples', the meaning of 'l2' or 'iterations', or the practical scope of the model. This is critically inadequate for safe and correct invocation.

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 input schema has 0% description coverage, so the description must compensate by explaining parameter meanings. The only parameter hinted at is 'scope' via the adjective 'scoped', but 'l2' (likely a regularization strength) and 'iterations' are completely unexplained. Furthermore, the description mentions 'explicit labelled examples' as input, yet there is no parameter for providing data, creating confusion about how the model is actually fitted.

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 'Fit' with a resource 'scoped logistic evidence-dependence model' and specifies the input source ('explicit labelled examples'). This clearly distinguishes it from sibling tools like athena_evidence_dependence_predict or athena_evidence_dependence_observe, which are inference/observation operations. However, the term 'scoped' and 'evidence-dependence' are domain jargon that could be clearer, preventing a 5.

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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention that this is a model-fitting operation that should be followed by prediction tools, nor does it mention any prerequisites or exclusions. The agent is left to infer usage purely from the verb 'Fit' and the sibling names.

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