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

Annotations already establish readOnly=true, destructive=false, openWorld=false, so the safety profile is covered. The description does add real semantic context not in the annotations — that runs only use enabled models — but it says nothing about ordering, pagination, or what the response looks like beyond the listed fields.

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 sentences, no filler, and the core action plus returned fields are front-loaded. The trailing sentence about enabled models is short and carries genuine information rather than restating the name.

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 no output schema, the description must carry the return shape, and it does list the meaningful fields (weight, transport, enabled). It falls short only on ordering and size/volume expectations, which are minor for a single-workspace listing.

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?

Only one parameter and schema coverage is 100%, with the schema itself telling the agent to fetch the ID from list_workspaces. The phrase 'in this workspace' reinforces the scoping but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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?

It names a specific verb (List) and resource (trackable AI models) scoped to a workspace, and it enumerates the fields returned — per-run answer weight, transport, enabled status. That is enough to distinguish it from generic reads, though it never explicitly contrasts itself with the nearby set_workspace_models sibling.

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 when-to-use, when-not-to-use, or alternative guidance. The description never points to set_workspace_models as the mutation counterpart, even though it is the obvious adjacent tool. Usage is only inferable from the wording.

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