athena_gp_bma_predict

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

No annotations are provided, so the description must carry behavioral transparency. It adds meaningful context by stating that the output separates within-model and between-model variance, and that averaging is over a finite posterior. However, it does not disclose prerequisites, side effects, or return format, leaving a gap.

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 with no redundant words. It packs a precise definition into 14 words, so it earns a high score for structure.

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?

Given the tool's complexity (4 parameters, nested objects, no output schema) and the complete absence of annotations, the description alone is insufficient. It gives a high-level purpose but leaves parameter meanings, return values, and usage prerequisites unexplained. Thus it falls short of a complete tool definition.

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 provides no explicit mapping to the 4 parameters (context_key, features, candidates, include_observation_noise). The reference to 'GP prediction' could relate to features, but no semantic details are given, so the description fails to compensate for the schema gap.

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 the specific verb 'Bayesian-model-average' and identifies the resource as 'a GP prediction across the finite hyperparameter posterior.' It clearly distinguishes from siblings like athena_gp_predict or athena_gp_sparse_predict by specifying the BMA and finite posterior aspect. The variance separation detail adds further specificity.

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: it is for use when a finite hyperparameter posterior exists. It does not explicitly name alternatives or exclusion criteria, but the scenario is clear. Sibling tools like athena_gp_predict are not referenced, which would have made the guidance more explicit.

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