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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is carried structurally. The description adds provenance context — 'These are measurements, the yardstick the site's own model is checked against' — and explains why vendor/device names appear (agent-facing only), which goes beyond what annotations convey.

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 dense but well-organized: purpose first, payload second, usage context third. Sentences about the cone constraint, arXiv reference, and agent-facing rationale each add precision or behavioral context rather than padding, though the notation requires some effort to parse.

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 carries the full burden of explaining the return payload, and it enumerates the per-device fields (circuit size, polarization threshold, cone bound, width, QUOPS rate, architecture variants, source URL) plus the utility-scale targets. For a zero-parameter read-only list tool, nothing an agent needs to call it correctly is missing.

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 tool exposes zero parameters and the schema confirms this with 100% coverage, so there is no parameter semantics to add and the baseline 4 applies. The description instead devotes its space to the return payload, which is the more valuable information for a parameterless tool.

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 verb 'Return' plus the resource 'measured QUOPS capability scores' states exactly what the tool does, and the payload is enumerated precisely (per device, circuit size, polarization threshold, cone constraint, width, QUOPS rate, architecture, source URL, targets). The measurement-vs-model framing distinguishes it from compute_expectation and the other compute_* siblings without ambiguity.

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 explicitly contrasts measured values with model estimates by directing the agent to 'modelCapability.quopsEquivalent on the compute_expectation hardware context', effectively naming the sibling to use when a modeled value is needed. It does not provide a formal when-not-to-use list, but the measurement-vs-model distinction gives sufficient selection context.

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