blender_measure_clearance

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

With no annotations provided, the description carries the full burden, and it does substantial work: it discloses that the tool computes three distance statistics, detects intersection/overlap, and returns the closest point pair. It also implies a read-only measurement operation, which is appropriate for this tool. It could additionally describe the sampling nature or return format, but the core behavioral traits are clearly communicated.

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 three concise sentences with no filler. It front-loads the core measurement behavior, then adds the intersection/closest-point output detail, then ends with a practical use case. Every sentence earns its place.

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 appropriately communicates what the caller should expect: distance statistics, intersection detection, and closest point pair. The parameter schema covers the sampling behavior, and the use case helps disambiguate from sibling tools. A fully explicit return structure or unit explanation would improve completeness, but the current description is sufficient for reliable invocation.

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%, and the input schema already documents all three parameters, including the accuracy/performance tradeoff of sample_count. The tool description adds no parameter-level detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 a specific verb ('Measure') and resource ('two mesh objects'), and spells out the exact quantities returned: minimum, average, and maximum distance. It also signals its distinct role among measurement siblings by mentioning intersection detection and collision/clearance checks, which separates it from surface area, volume, and edge angle tools.

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 closing sentence, 'Useful for collision/clearance checks', gives a clear use-case context that helps an agent decide when to invoke this tool. It does not explicitly name alternative measurement tools or state when not to use it, but the context is specific enough to guide selection among the many sibling measurement tools.

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