ray_cluster_resources

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

No annotations are provided, so the description carries the burden. It includes a '[READ]' prefix indicating read-only behavior, but it does not elaborate on permissions, side effects, or other behavioral traits. The description is adequate but not thorough.

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 concise: a single line for purpose and one line for parameter explanation. It is front-loaded and efficient. Minor room for structure improvement but very effective for a simple tool.

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?

With no output schema, the description partially covers return values by listing CPU/GPU capacity, headroom, and pending placement groups. However, it lacks broader context such as assumptions, required resources, or limitations. Adequate but not exhaustive given the tool's simplicity.

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 schema coverage is 0% per the metric, but the description adds meaning beyond the schema by explaining that 'target' is an inference target name from config and that omitting it uses a default. This clarifies usage despite the schema showing only a string/null type.

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 reads cluster-wide CPU/GPU capacity, headroom, and pending placement groups. The verb 'read' is explicit, and the resource scope (cluster-wide) is specified. This purpose is distinct from sibling tools like gpu_utilization or ray_dashboard_status.

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 provides no guidance on when to use this tool versus alternatives. It does not specify prerequisites, when-not-to-use, or scenarios where sibling tools are more appropriate. The only usage hint is about the target parameter being optional.

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