queue_depth

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

The '[READ]' tag indicates read-only behavior, but with no annotations, the description should also mention that it is non-destructive and safe to call. It does not disclose other behavioral traits like error states, performance impact, or caching, but is minimally 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?

Two-line description with a front-loaded tag, concise purpose statement, and parameter documentation. No unnecessary words.

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 covers purpose and parameter, but lacks explicit mention of return format (e.g., numbers, structure). For a simple tool with no output schema, it is adequate but could be more complete.

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 coverage is 0%, but the description documents the 'target' parameter as an optional inference target name, adding meaning beyond the schema. The default behavior ('omit for the default') is explained.

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 reads queue depth for an inference target, using the verb 'Running vs waiting requests' and the tag '[READ]'. It distinguishes from siblings like engine_queue_depth by implying it's a generic queue depth signal for the inference target.

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 on when to use this tool versus alternatives like engine_queue_depth, request_metrics, or model_is_sleeping. No explicit context for when it is appropriate or inappropriate.

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