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

With no annotations, the description carries the full burden. It usefully discloses that results come from measured-anchored curves and that the answer carries basis (measured/interpolated/estimated), confidence and evidence boundaries, which is meaningful behavioral context. However, it does not clarify read-only safety, whether any parameters are required (schema says none), precedence between the free-text 'q' and structured fields, or defaults - notable gaps for a tool with zero required params and no annotations.

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 dense sentences with the core purpose front-loaded and no filler. The append about basis/confidence/evidence boundaries earns its place, though the phrasing is slightly jargon-heavy.

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?

For a tool with no output schema and six optional parameters, the description partially covers the return (basis, confidence, evidence boundaries) but does not spell out the primary output field or how ambiguity among optional inputs is resolved. This is adequate but leaves meaningful gaps an agent would need to discover by trial.

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 100%, so the parameter meanings are already fully documented, including defaults for ctx, batch and precision and that batch>1 savings are modelled rather than measured. The description adds only the framing of model size/GPU architecture/precision as the estimation inputs, which is close to baseline for a fully-covered schema.

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?

States a specific verb and resource ('Estimate the task-level energy change (ΔE% vs FP16) of weight-only quantization') with a precise output quantity and input scope. It is clear what it does, but it never mentions the sibling get_quantization_crossover or explains why one would pick this estimator over that tool.

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?

Usage is implied by the tool's purpose (estimating quantization energy savings), but there is no explicit when-to-use/when-not-to-use guidance and no reference to the crossover or measurement-search siblings that an agent would plausibly confuse it with. It also does not say whether a plain-English query is preferred over structured parameters.

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