detect_supportedness

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

The readOnlyHint=true and destructiveHint=false annotations already communicate that this is a safe read-only operation, so the description does not need to cover side effects. It adds meaningful context by indicating the output is a percentage and clarifying what 'unsupported' means via PreForm's red shading, but it does not disclose return structure, scene handling, or behavior when no models are present.

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, front-loaded sentence that defines both the metric and the visual reference without wasted words. Every part earns its place: the subject, the measurement, and the clarifying parenthetical.

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 simple read-only diagnostic with one optional parameter and no output schema, the description provides the core output semantics: a per-model percentage of unsupported surface. Minor gaps exist around the exact return container and how the optional scene_id affects the result, but these are not severe enough to undermine correct 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?

The schema provides 100% coverage for the only parameter, scene_id, including its default and description. The tool description adds no parameter information beyond that, so the baseline score of 3 is appropriate; the schema already carries the full semantic weight.

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 identifies what the tool measures: the percentage of each model's surface that is unsupported, with PreForm's red shading as the reference. It is specific about the metric and resource, but lacks an explicit verb like 'calculates' or 'returns,' and it does not explicitly distinguish itself from sibling detect_* tools such as detect_cups or detect_minima.

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: an agent can infer this tool is for assessing unsupported surface coverage, likely during print validation. However, there is no explicit statement about when to prefer this tool over related checks like detect_cups, detect_minima, or get_print_validation, and no alternatives or exclusions are mentioned.

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