list_variables

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

Annotations already declare readOnlyHint=true, so safety is handled. The description adds useful context by specifying it covers stocks, flows, and auxiliaries, and that it operates on the current model. No behavioral surprises are hidden, though it doesn't mention pagination or any performance aspects.

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 scope. Every word earns its place, with no redundancy or filler.

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 is a simple read-only list operation with full schema coverage and an output schema present, the description is sufficiently complete. It tells the user what it lists and the scope, while the output schema covers return details. Minor gaps like absence of any mention of filtering or ordering are acceptable for such a 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?

Schema description coverage is 100%, with both parameters (model_id and workspace_id) already described in the input schema. The description adds no additional parameter-level information, keeping the score at the baseline of 3.

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 all variables (stocks, flows, auxiliaries) in the current model, using a specific verb ('List') and resource ('variables'). It distinguishes from sibling tools like list_models or add_variables by specifying scope and variable types.

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 provides clear context for when to use the tool: to list variables in the current model. It doesn't explicitly mention alternatives or exclusions, but given the sibling tool set, no other tool performs this exact listing function, making the usage context clear enough.

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