get-trained-model-versions

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

No annotations are provided, so the description carries the full burden. It discloses only the basic read action ('List') and does not mention potential behavioral traits such as pagination, limits, ordering, authentication requirements, or response format. While 'List' implies read-only, the description adds no useful behavioral context beyond the verb itself.

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 short sentence, 'List all trained model versions,' with no extraneous words. It is front-loaded with the action and resource, perfectly concise for the tool's simplicity.

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 tool with no parameters and no annotations, the description is reasonably complete: it states the core purpose. However, with no output schema, it could briefly mention what is returned (e.g., a list of version identifiers or metadata), though 'List' already implies a collection. Minor gaps exist around scope and result details, but overall it is adequate for such a simple operation.

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, and the schema is empty, so there are no parameter semantics to clarify. Per the baseline for 0 params, a score of 4 is appropriate; the description does not need to explain nonexistent parameters, and it adds no misleading information.

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 'List all trained model versions' uses a specific verb (List) and resource (trained model versions), clearly distinguishing itself from the sibling tool 'get-trained-model-version' (singular) by specifying 'all'. It leaves no ambiguity about what the tool does.

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 alternatives such as 'get-trained-model-version' (singular) or 'delete-trained-model-version'. The description simply states the action without mentioning that this tool is for enumerating all versions while the singular variant retrieves a specific one. No explicit conditions or exclusions are provided.

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