athena_gp_hyperposterior

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

No annotations are provided, so the description carries the transparency burden. It discloses 'Read-only' and specifies the computation method (marginal likelihood and visible priors), which is helpful. However, it does not describe the return value or potential errors, and since there is no output schema, this is a notable omission.

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, front-loaded sentence that directly states the action and safety. Every word contributes meaning, with no filler or redundancy.

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?

The tool has no output schema and no annotations, so the description must explain return values and parameter semantics. It covers the core computation and read-only nature, but omits the return format and leaves a required parameter ('context_key') undocumented. This makes it incomplete for an agent to invoke 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?

With 0% schema coverage, the description must compensate. It relates 'candidates' to the hyperparameter grid, but it does not explain the 'context_key' parameter or the expected structure of the candidate objects. This incomplete coverage leaves the agent guessing on key input details.

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 the tool computes a normalized posterior over a GP hyperparameter candidate grid, using a specific verb ('Compute') and resource. It distinguishes itself from siblings like athena_gp_hyperfit by explicitly focusing on posterior calculation rather than fitting.

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 clear context for when to use the tool: when a normalized posterior over a finite explicit candidate grid is needed. It does not explicitly mention alternatives or exclusions, so it stops short of a 5, but the applicability is evident.

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