athena_gp_predict

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

With no annotations provided, the description carries the full burden and does well by disclosing that it returns posterior mean/variance, uses exact fixed-hyperparameter RBF GP, and never self-trains. This goes beyond the schema and gives important behavioral context. It lacks details on prerequisites or error states, but the core behavior is transparent.

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 only two sentences, front-loaded with the core function, and every word contributes value. The second sentence adds a critical behavioral trait without redundancy. This is ideal conciseness.

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 description covers the essential purpose and a key behavioral trait, but with no output schema and no parameter explanations, it leaves gaps. It doesn't clarify what 'context_key' refers to (e.g., a registered GP model), how to construct 'features', or what the return format looks like beyond mean/variance. For a prediction tool with three parameters, this is adequate but not complete.

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%, so the description must explain the parameters, but it doesn't. It never mentions what 'features', 'context_key', or 'include_observation_noise' mean or how they affect the prediction. The only hint is 'for one query', which vaguely aligns with 'features', but no actual parameter semantics are provided.

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: 'Return exact fixed-hyperparameter RBF GP posterior mean/variance for one query.' This uses a strong verb, specifies the resource (GP posterior), and narrows scope (exact, fixed-hyperparameter, RBF, one query). It distinguishes from siblings like sparse or hyperfitting GP tools.

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 when to use this tool (exact GP prediction with fixed hyperparameters) and explicitly says it never self-trains, which hints against using it for hyperparameter fitting. However, it does not name alternative tools or provide explicit when-not-to-use conditions, leaving the agent to infer based on sibling names.

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