local_gpu_discover_models

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

With no annotations, the description must disclose behavioral traits, but it only mentions that model weights are not loaded and that discovery is bounded. It does not explain side effects, phases, authorization requirements, confirmation flows, or network behavior even though schema parameters hint at these. The description adds some value but is far from transparent.

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, front-loaded sentence that avoids redundancy and directly conveys the core action. However, it is under-specified for the tool's complexity, but that is more a completeness issue than a structural problem. It earns its place without waste.

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?

Given the tool has 12 parameters, multiple phases, stages, and confirmation/authorization fields, the description is inadequate. It omits the meaning of 'plan' vs 'execute', the discovery stages (index/fingerprint/verify/revoke), and the scope modes. Even though an output schema exists, the description does not provide enough context to use the tool 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?

Schema description coverage is 0% with 12 parameters, and the description does not explain any of them. The phrase 'bounded' and 'without loading model weights' gives general context but no specific parameter meanings. The description fails to compensate for the complete lack of parameter documentation.

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's function: planning or executing bounded local model discovery without loading model weights. The verb 'plan or execute' combined with the resource 'local model discovery' is specific, and the qualifier 'without loading model weights' adds a distinctive safety constraint. However, it does not explicitly differentiate from sibling tools like local_gpu_recommend_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?

The description implies usage for model discovery when avoiding weight loading is desired, but it lacks explicit when-to-use or when-not-to-use guidance and does not mention alternatives. There are no prerequisites or exclusions stated, leaving the decision to the agent without additional context.

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