athena_gp_hyperfit

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

With no annotations, the description must disclose side effects and state changes. It mentions 'optional CAS application to current GP model' but does not explain what CAS entails, whether the tool is destructive, what state changes occur when 'apply' is true, or any permission requirements. This leaves significant behavioral ambiguity.

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 leads with the main action. It avoids verbosity, though the unexplained 'CAS' acronym introduces some ambiguity without extra length. Overall it is efficient and well-structured.

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 6 parameters, no annotations, and no output schema, so the description must carry substantial contextual load. It does not explain return values, the effect of the 'apply' flag, the role of context_key, or the significance of the hyperparameter arrays. The description is insufficient for an agent to invoke the tool safely and correctly.

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%, yet the description adds minimal detail about the parameters. It references 'hyperparameters' generically but does not explain the meaning of length_scales, noise_variances, signal_variances, apply, expected_observation_count, or context_key. The description fails to compensate for the schema's lack of descriptions.

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 a specific action: grid-searching fixed RBF GP hyperparameters via exact marginal likelihood. This identifies the verb, resource, and method, and distinguishes it from sibling GP tools (e.g., hyperposterior, hyperqmc) that address different inference tasks.

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 guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description implies it is for hyperparameter search but does not explain scenarios where another tool would be preferred or how to decide between grid-search and other hyperparameter approaches.

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