List Models

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

Annotations already declare read-only, idempotent, non-destructive, and open-world behavior. The description adds useful context about the content: models include pricing, video i2v duration constraints, and the watermark exception. This goes beyond the structured annotations.

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 well-structured with a clear opening statement, a compact enumeration of model categories, specific notes about video constraint fields and watermark behavior, and a brief arg summary. It is slightly redundant with the schema in the Args section but remains appropriately sized.

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?

Without an output schema, the description compensates by mentioning pricing info, model categories, video duration constraints, and the watermark exclusion. It gives enough context for the agent to understand what will be returned. Minor gap: it doesn't specify exact response structure or pagination, but that's acceptable for a simple 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?

The input schema already provides a full description of the category parameter, including the valid enum values and the meaning of omission. The description's Args section merely repeats the schema's coverage, adding no new semantic value. Baseline 3 is appropriate since schema coverage is 100%.

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 available XBrush AI models with pricing info, using a specific verb and resource. It distinguishes itself from sibling tools like list_voices and the various generation tools by focusing on model metadata rather than execution.

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 includes explicit guidance: it notes that watermark models have no dedicated list and should be called directly, providing a when-not usage. It also implies the tool is for discovering models before invoking generation/chat calls, and the category filter offers a way to narrow results.

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