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

No annotations are provided, so the description must cover behavioral traits. It only states the action ('close'), leaving uncertainties about side effects (e.g., memory release, undoability). No disclosure of whether this is destructive or reversible.

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 extremely concise: one sentence, plus aliases and a single example. Every element 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?

Given the tool's simplicity (one optional parameter, output schema present), the description is minimally complete. It covers the basic operation but omits details like what happens when closing 'all' or potential error conditions.

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 must clarify parameter meaning. It implies 'model_id' identifies which model to close, with 'all' as default, and gives an example using '#1'. However, the format of 'model_id' (e.g., '#1' vs a name) is not explained, leaving ambiguity.

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's purpose: 'Close one or all open models.' The aliases ('remove model, delete model, unload') reinforce the verb and resource. This distinguishes it from sibling tools like 'delete_atoms' which operate on atoms rather than models.

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?

An example usage is provided ('close_structure("#1")'), which implies how to target a specific model. However, there is no explicit guidance on when to use this tool versus alternatives, nor any mention of prerequisites or exclusions.

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