model_list

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

The '[READ]' prefix indicates a read-only operation, which is helpful since no annotations are provided. However, it does not disclose other behavioral traits such as whether it requires authentication, whether it's expensive to call, or what happens if no models are served. The description partially compensates for missing annotations but lacks depth.

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 extremely concise, with only two lines: a clear '[READ]' indicator and a one-line parameter explanation. Every sentence serves a purpose, and it is front-loaded with the key action.

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 lacks information about the return format (expected schema of output) and does not provide context for when to use this tool over siblings like model_info or engine_inventory. Given the absence of an output schema and the number of sibling tools, the description is incomplete for an agent to make fully informed decisions.

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 description explains the 'target' parameter as 'Inference target name from config; omit for the default', adding meaningful context beyond the schema's type-only definition (string or null). This helps an agent understand how to use the parameter correctly, especially since schema description coverage is 0%.

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 it lists all served vLLM models and LoRA adapters, using a '[READ]' prefix for clarity. It distinguishes itself from sibling tools like model_info (which likely returns details for a specific model) and model_deploy/undeploy (mutations).

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?

No guidance is provided on when to use this tool versus alternatives (e.g., model_info for details or model_is_sleeping for status). The agent is left to infer usage from the tool name and the '[READ]' hint, but no explicit when/when-not advice is given.

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