List GLM models

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

With no annotations, the description carries the behavioral burden. It discloses useful behavior: each model id is annotated with a one-line role, and unknown ids are rendered bare. It does not mention authentication or rate limits, but for a simple read-only enumeration this is not a significant gap.

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?

A single sentence that front-loads the action and includes only essential details about the output format and the special handling of unknown ids. Every clause adds value with no 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 list tool with no output schema, this description is complete: it tells the agent what will be returned, how roles appear, and how unknown ids are presented. Nothing critical is missing for invoking and interpreting the tool.

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 an empty input schema, so there is nothing for the description to clarify. The baseline of 4 applies because the schema already fully covers parameter semantics.

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?

States a specific verb and resource: lists GLM model ids from the configured Z.ai account. It also adds useful output semantics (one-line role, unknown ids listed bare), which makes it clearly distinct from sibling tools glm_ask and glm_review.

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 makes the context clear: use this tool when you need to enumerate available model ids. It does not explicitly name alternatives or when-not-to-use, but the sibling tool names and the list-oriented wording make the intended usage obvious enough.

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