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

No annotations are provided, so the description must disclose behavioral traits. It states that a 'real' inference is run, implying actual execution and potential side effects, and describes the report output. However, it does not mention safety, cost, latency, or whether it mutates any state, leaving some ambiguity.

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 efficiently conveys the action and outcome. Every word adds value with no redundancy.

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's simplicity (0 params, no output schema), the description covers the core function. However, it lacks broader context about when this probe is appropriate and how it differs from sibling tools, which is important for an agent to select 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?

With zero parameters, the baseline is 4. The description adds no parameter details, but none are needed since the input schema is empty and the tool operates without arguments.

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 uses a specific verb ('Run') and resource ('routed gateway inference') and clearly states the output ('report the exact-model ZeroGPU fallback state'). It distinguishes itself from siblings like gateway_health and list_models by focusing on a real inference probe rather than a static check or listing.

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 the use case: when you need to determine the exact-model ZeroGPU fallback state via a real routed inference. However, it provides no explicit guidance on when to use this tool versus alternatives like gateway_health or provider_rank, and no exclusions.

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