model_list

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

readOnlyHint=true already declares the safety profile, so the '[READ]' tag is largely redundant. The description does add that LoRA adapters are flagged in the output, which tells the agent something about content. No contradiction, but limited extra behavioral context beyond 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?

Front-loads the purpose in one line, then the parameter note. Short and efficient with minimal waste, though the '[READ]' prefix duplicates the annotation and adds little.

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, annotation-covered list tool with no output schema, the description covers what it returns (served models with LoRA flagged) and how the single optional parameter behaves. Sufficient to invoke correctly, though auth/prerequisite context is absent.

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%, so the description carries the burden here, and it does: it explains 'target' as an inference target name from config and states that omitting it uses the default. That meaningfully compensates for the undocumented schema parameter.

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?

States a specific verb (list) and resource (served vLLM models) with scope ('all served'). It implicitly contrasts with model_info (single-model detail), though it never names the sibling explicitly. Clear purpose without explicit sibling differentiation.

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 explicit when-to-use or when-not-to-use guidance, and no alternatives named among the many model_* siblings (model_info, model_deploy, model_undeploy). Usage is only implied by the '[READ] All served models' phrasing, with a small hint that omitting target selects the default.

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