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

Annotations already mark the tool read-only, idempotent, and non-destructive. The description adds behavioral context beyond that: the known set is built-in/closed, standard text-only HuggingFace transformer configs are additionally checkable, and unsupported architectures are rejected. No contradiction with annotations.

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?

Two tightly written sentences with no filler. The first sentence front-loads the action and purpose, and the second adds eligibility and rejection behavior without redundancy.

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?

For a zero-parameter, read-only list operation with annotations already covering safety, the description is complete: an agent knows what will be listed, why it exists, and what constraints apply to checkable configs. The return shape is self-ident from the listing semantics.

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 tool has zero parameters, so no parameter documentation is needed; the zero-parameter baseline applies. The description still adds useful context about what the returned names are for, which is sufficient.

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?

Clearly states a specific verb ('List'), a specific resource ('built-in model names and hardware names'), and an explicit purpose ('mapping user wording to exact names'). This also distinguishes it from the sibling fit-checking tools, which answer different questions.

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 identifies a clear use context: when an agent needs to map user wording to exact built-in model or hardware names. It does not explicitly name sibling tools or provide when-not-to-use guidance, but the stated purpose makes the boundary sufficiently clear.

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