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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the method (pixel ratio), the AI's role (Vision analysis and passing pixel values), supported reference object dimensions, and confidence handling. It does not fully detail edge cases or the exact return structure, but it covers the key behavioral traits.

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-structured: usage trigger, calculation method, AI role, supported reference list, downstream workflow, and confidence fallback. Each sentence provides useful information, though it is slightly long. It is appropriately sized for the tool's complexity.

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?

This is a complex tool with 7 parameters, nested objects, and no output schema. The description covers the full workflow: what to pass (pixel dimensions), what the tool computes, how to use the result, and what to do in low-confidence cases. It gives sufficient context for an agent to invoke it correctly and use its output.

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 description coverage is 100%, so the baseline is 3. The description adds extra meaning by listing actual dimensions for each supported reference object (e.g., 名刺 91×55mm), clarifying that pixel values are to be read by Vision, and explaining manual_dimensions_mm as an override. This goes beyond the schema's basic descriptions.

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's function: 'ピクセル比率から対象物の実寸(mm)を逆算する' – it estimates actual dimensions from a photo with a reference object. It uses specific verbs and distinguishes itself from sibling tools (which handle search, comparison, or storage) by being the only measurement 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?

It explicitly states when to call: '「写真を撮ったので寸法を測りたい」「この隙間に合う棚を探したい」のときに呼ぶ'. It also provides integration guidance: passing search_dimensions to suggest_by_space or coordinate_storage, and advises telling the user to measure manually if confidence is low.

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