List VLM Models

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

No annotations are provided, and the description does not explicitly state that this is a read-only operation or mention any side effects, permissions, or rate limits. Although a GET request implies safety, the description lacks an explicit transparency statement.

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 resource. It includes the API endpoint as useful context without any redundant or vague wording.

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?

The description is sufficient for a simple list operation, but it does not detail what the response contains (e.g., model names, prompts, capabilities). Since no output schema is provided, a brief mention of the returned data would improve completeness, but it is not critically missing.

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?

There are no parameters in the input schema, so schema coverage is 100%. The description does not add parameter-related information, but since none exist, the baseline of 3 is appropriate.

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 (vision-language models), and includes the specific HTTP endpoint. It distinguishes itself from sibling list tools (e.g., samplers, schedulers) by explicitly naming VLM 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?

While the description does not explicitly say 'use this when you need VLM models', the specificity of the resource makes the intended usage obvious. It does not mention alternatives, but for a simple list operation, the context is clear enough to guide selection.

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