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

No annotations are provided, so the description must fully convey behavioral traits. It indicates the tool is a read-only 'glass-box' analysis, but it does not mention permissions, rate limits, or error handling (e.g., what happens if evaluation_id is invalid). The description is adequate but not exhaustive.

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, well-structured sentence that directly states the tool's purpose and contents. Every word contributes value, making it efficient and easy to parse.

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 simplicity (one parameter, no output schema), the description sufficiently explains the function and return components. It could mention what happens on failure or the absence of a result, but for a straightforward breakdown tool, it is largely 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?

The single parameter evaluation_id is described as 'prior evaluation by evaluation_id', adding context that the ID corresponds to an existing evaluation. However, it does not specify the expected format or how to obtain valid IDs. Schema coverage is 0%, so the description provides some additional meaning beyond the raw schema but could be more precise.

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 it provides a 'Glass-box breakdown' of a prior evaluation by evaluation_id, listing specific components (sub-signals, weight, floor, composite reasoning). This distinguishes it from siblings like alphalabs_calibration_report or alphalabs_feature_attribution, which serve different purposes.

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 use when a detailed breakdown of a prior evaluation is needed, but it does not explicitly state when to avoid this tool or suggest alternatives. For example, if only the final outcome is needed, alphalabs_outcome_report might be more appropriate. The context is clear but lacks exclusionary guidance.

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