List model aliases

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

Annotations already establish that this is a read-only, idempotent, non-destructive operation, so the bar is lower. The description adds useful behavioral context beyond that: each alias resolves to exactly one version, previousModelVersionId is null for a new alias, and entries include timestamps and a ui_url.

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 front-loaded around the core definition and then adds ecosystem context, with no obvious filler. It is slightly longer than strictly necessary because it enumerates return fields despite an existing output schema, but the structure remains efficient.

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?

With an output schema present, the return-field enumeration is somewhat redundant but reinforces correctness. The annotations cover the safety profile, and the description supplies the conceptual model plus related tools, so an agent can call this correctly; only the input parameter semantics are under-specified.

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%, so the description should compensate for two required parameters. It explains the concept of a model's aliases but never clarifies the role, format, or lookup expectations of project_id and model_id, leaving the input schema to carry undocumented parameters.

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 uses a specific verb and resource ('List a model's aliases') and immediately defines what an alias is: a named pointer such as staging or production resolving to exactly one version. It is clearly distinguishable from siblings like set_model_alias and list_model_versions, which are named explicitly.

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?

It explains the role of aliases in the broader workflow, naming run_prediction, run_batch_prediction, and set_model_alias as the consumers/mutators of aliases, and notes that list_model_versions also exposes aliases. This gives clear contextual guidance even though it does not state explicit 'use this when' exclusions.

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