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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'List,' which hints at a non-destructive read, but discloses nothing about return format, ordering, coordinate edge cases, or performance.

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 sentence with zero filler, front-loaded with the verb 'List.' Every word earns its place and the sentence communicates the tool's essential function immediately.

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 6 parameters, 0% schema description coverage, no annotations, and no output schema, the one-line description leaves too much unspecified: parameter semantics, required-vs-optional meaning, and return shape. An agent would struggle to construct a correct call without additional external 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 0% for 6 integer parameters, and the description must compensate. It gives the geometric context of an 'axis-aligned box' but does not explain how x, z, w, d, y, h map to coordinates and dimensions, nor which parameters are optional beyond what the required list implies.

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 states a specific verb ('List') and resource ('cubes') with a clear scope ('inside an axis-aligned box'), so an agent can recognize it as a read-style region query. However, it does not explicitly differentiate itself from the sibling tool 'get_observable', even though the resource and operation make the purpose reasonably distinct.

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?

There is no guidance on when to use this tool versus the sibling 'get_observable' and no mention of exclusions or preconditions. The description only implies a use case (querying cubes in a box) without any decision context.

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