list_model_variables

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

No annotations are provided, so the description carries full burden for behavioral transparency. It only mentions listing and optional filtering, lacking details about side effects, authentication needs, rate limits, or whether the operation is read-only. The description is insufficient for a no-annotation scenario.

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 a single, concise sentence that front-loads the action and key feature (optional filtering). No unnecessary words.

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 an output schema present, return values are covered, but the description lacks context about the scope of 'kVARS' (e.g., which model), pagination, or limits. It is adequate but incomplete for a list 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?

Although the input schema has 0% description coverage, the description clarifies that the 'filter' parameter performs substring matching, adding meaning. However, it does not provide details on case sensitivity or format, so it only partially compensates.

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 action (list) and the resource (kVARS model/output variables), and mentions optional substring filtering. This effectively distinguishes it from sibling tools like list_namelist_groups or search_model_variables.

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 implies a use case (listing with optional filter) but provides no guidance on when to prefer this tool over alternatives like search_model_variables or get_model_variable. No exclusion criteria or context for selection.

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