compare_measurements

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

With no annotations provided, the description carries the full burden. It discloses the output form (per-band summary, largest divergences) and the meaning of sign ('Positive values mean A is hotter'), which is meaningful behavioral context. It does not mention read-only status or side effects, but for a comparison tool the absence of mutation is inferable and the description does add useful non-obvious information.

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, well-crafted sentence that front-loads the purpose and packs in examples and output semantics without redundancy. Every clause adds useful information, achieving maximum conciseness without sacrificing clarity.

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 no output schema, the description adequately summarizes the return content (difference curve per band, largest divergences). It covers the core behavior and provides context for typical use. It could be more explicit about how 'per band' is determined or how maxPoints affects results, but those are partially covered by the schema and domain conventions, making the description reasonably 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 100%, so the baseline is 3. The description adds extra value by clarifying the directional relationship between measurementA and measurementB through 'Positive values mean A is hotter', which helps the agent understand parameter orientation. This exceeds simple schema repetition.

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 'compare' with a clear resource ('two measurements') and specifies the output ('A-minus-B difference curve summarised per band, plus the largest divergences'). The examples (before/after EQ, left/right symmetry) and the direction semantics ('Positive values mean A is hotter') distinguish it from sibling tools like arithmetic or average_measurements.

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 concrete usage contexts ('e.g. before/after EQ, left/right symmetry'), making it clear when comparison is appropriate. It does not explicitly state exclusions or name alternative tools, but the examples imply the intended use case, which is more than vague guidance.

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