aetre_multi_attribute_voi

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It states the tool computes and outputs, but does not describe any side effects, limitations, assumptions (e.g., orthogonality as a requirement), error conditions, or the structure of the returned data. The behavior is essentially self-evident from the name and schema, adding little beyond the obvious.

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, dense sentence that front-loads the core functionality ('Computes multi-attribute Bayesian VOI') and then explains the context and outputs. It contains no redundancy or filler. It is slightly long but remains 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?

The tool has multiple parameters, a nested array, and no output schema. The description mentions the output shape conceptually (composite VOI and optimal review targets) but does not specify the return format, pagination, or any behavioral nuances. Given the complexity of the tool, the description is adequate but leaves gaps around output details and usage context.

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 100%, so the baseline is 3. The description adds context by listing example dimension names (Novelty, Rigor, Impact, Feasibility) and the property 'orthogonal', which hints at independence between dimensions. However, it does not elaborate on the other parameters (composite_threshold, review_cost_per_dim) beyond what the schema provides. The added meaning is marginal.

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 verb 'Computes' and the resource 'multi-attribute Bayesian Value of Information', with specific context about dimensions (Novelty, Rigor, Impact, Feasibility). It also mentions the output (composite VOI and optimal dimension-specific review targets). It does not explicitly name a sibling, but the 'multi-attribute' and 'orthogonal' wording differentiates it from likely single-attribute variants like aetre_calculate_voi.

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 guidance is given on when to use this tool as opposed to alternatives. It does not mention when this should be preferred over aetre_calculate_voi, aetre_correlated_posterior_update, or aetre_heavy_tailed_voi, nor does it state any conditions or exclusions. The description is purely functional.

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