list_models

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

With no annotations provided, the description carries the full burden. It discloses the return structure (array of objects with name, label, cost, paidOnly) and instructs the agent to use `name` as the engine/model value. This is meaningful above a simple purpose statement, though it doesn't cover error handling or auth requirements.

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?

Three sentences deliver purpose, usage, and return contract with no fluff. Information is front-loaded, making it easy to parse.

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 simple list operation with one optional parameter and no output schema, the description covers why to call it, when to call it, and what it returns. The schema handles the parameter definition, so nothing critical is missing; it could mention pagination or filtering behavior, but those aren't applicable here.

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% for the single 'category' parameter. The description mentions 'for a category' but doesn't elaborate on allowed values, defaults, or how the category affects the results beyond filtering the list; the enum and default are only in the schema, and no additional context is added.

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 identifies the tool as listing available generation engines/models with their credit costs per category, and explicitly instructs to use it before generating to avoid hardcoding engine names. This distinguishes it from sibling tools like get_credits (balance) and list_animation_presets.

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 explicitly states 'Call this before generating instead of hardcoding engine names,' providing a clear when-to-use rule. It does not mention alternative tools or when not to use it, so it falls short of full alternatives guidance.

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