athena_nonlinear_observe

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that the tool updates models, but does not mention side effects, persistence, idempotency, or any potential consequences. This is a minimal disclosure for a mutation 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 sentence that is front-loaded and free of unnecessary words. It efficiently communicates the core action without fluff, though this brevity comes at the cost of completeness.

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?

For a tool with 9 parameters, 4 required, nested objects, no annotations, and no output schema, the description is grossly underspecified. It does not explain return values, side effects, or how to correctly invoke the tool, making it nearly impossible for an agent to use correctly without additional information.

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 fails to explain the meaning of any of the 9 parameters. It only conceptually hints at 'explicit observed reward' and 'raw-feature OOD reference distribution' but does not map these to specific parameters like reward, features, or regime. The description adds minimal value over the schema.

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 verb ('Update') and specific resources ('degree-2 Bayesian model', 'raw-feature OOD reference distribution'). It distinguishes itself from sibling tools like athena_transition_observe or athena_bayes_observe by naming the exact model and distribution being updated.

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 the tool is used when an explicit observed reward is available and the model/distribution need updating. However, there is no explicit guidance on when to prefer this over alternatives or when not to use it, leaving the context only vaguely implied.

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