List Model Groups

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

With no annotations, the description carries the full behavioral disclosure burden. It explicitly says 'Read-only; no side effects', reveals the underlying endpoint, and explains tagging overlap semantics. This is strong context beyond the bare operation, though it does not discuss error conditions or authorization.

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?

Three sentences with zero filler. The core action and safety cue are front-loaded, followed by return semantics and routing to alternatives. Every sentence earns its place.

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?

The description is complete for the tool's core purpose, behavior, and differentiation from siblings. The missing semantics of server_version is a minor contextual gap, and the presence of an output schema reduces the need to describe return values in prose.

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 schema already describes model_id, and the description reinforces it. However, the required server_version parameter has no schema description and is not mentioned in the description, leaving its role unexplained. Since schema coverage is only 50%, this is a notable gap.

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?

Description clearly states a specific verb and resource: lists the groups (tags) a given model belongs to. It also distinguishes itself from sibling tools by noting there is no per-model listing for systems and directing users to list_groups for system enumeration and workspace-wide tag listing.

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 gives explicit when-to-use guidance: use this for a model's tag memberships, and use list_groups(kind='system') for systems and list_groups(kind='tag') for all workspace tags. It also states the tool is read-only, making its safe use context clear.

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