List TraeCN Models

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's 'Read-only discovery' is redundant. However, it adds the specific detail that the tool returns the currently selected model alongside the list, which is beyond the annotations. This is useful behavioral context but not extensive.

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, purpose-first, with no fluff. The essential usage hint is included without padding. Every word 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?

This is a simple, parameterless, read-only tool with an output schema. The description fully covers what an agent needs: what it returns and when to call it. Nothing is missing for correct selection and invocation.

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?

With zero parameters and 100% schema description coverage, there is nothing for the description to add about individual parameters. The baseline of 4 for parameterless tools applies; the description makes no misleading claims.

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 clearly states the tool lists model names from the TraeCN UI plus the currently selected model. It uses a specific verb and resource, and the 'call before select_model' phrasing distinguishes it from the sibling selection tool.

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 explicitly instructs to call before select_model whenever the model choice matters, giving clear context for when to use it. It doesn't list when not to use it, but the guidance is direct and actionable for a discovery tool.

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