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 adds useful behavioral detail: results depend on backend reporting ('as far as the backend reports them'), and vLLM-specific behavior is disclosed (showing LoRA adapters and base models). This provides meaningful transparency beyond the simple operation name.

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, front-loaded with the core purpose, followed by return-value details and usage timing. No wasted words, and every sentence contributes meaningful information.

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 tool is simple, parameterless, and has an output schema, so the description need not re-explain return values. It covers what the tool lists, backend-specific behavior, caveats, and when to use it, which is complete for an agent to invoke it correctly.

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 tool has zero parameters, and the schema is empty, so there is nothing to document. The baseline of 4 applies because no parameter semantics are needed; the description is not lacking anything in this area.

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 states a specific verb ('Lists') and resource ('models available on the local instance'), and goes beyond a simple label by enumerating the included fields: name, parameter size, quantization, and on-disk size. This clearly distinguishes it from the sibling tools, which are about asking, structuring, and embedding.

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 gives explicit guidance: 'Sensible before any other tool, to pick a fitting and actually present model name.' This tells the agent when to call it, although it does not explicitly mention when-not-to-use or alternative tools, so it stops short of a 5.

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