rmx_scale

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

No annotations exist, so the description carries the full burden. It discloses the use of RMX Scaler with stroke-weight compensation, the opt-in native affine fallback and its limitation, the fact that RMX processes all masters even when master_id is set, and the response's method field. It does not state whether the operation modifies in place or returns a new glyph, which prevents a 5.

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?

Despite its length, the description is well-structured: a one-sentence purpose, a highlighted usage directive, a recipe hint, a concise behavior note, a bulleted Args section, and examples. Every sentence conveys necessary information with no filler, appropriate for a tool with 8 parameters.

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 8 parameters, no annotations, and no output schema, the description is nearly complete: it explains all params, the method field, fallback semantics, and provides examples. It lacks only explicit statement of whether the glyph is edited in place or returned as a new object, and how errors are surfaced, which would make it fully 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 description coverage is 0%, so the description must compensate entirely. It does: every one of the 8 parameters is documented with meaning and, for width/height, concrete percentages (100 = no change, 130 = 30% wider). It also explains list semantics for per-master control and provides example invocations. This is exemplary compensation.

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, resource, and scope: 'Scale a glyph by percentage in width and/or height.' It also gives canonical examples. However, it does not explicitly differentiate itself from the sibling smart_scale, which could plausibly perform scaling too, so it falls short of a 5.

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 gives explicit when-to-use guidance: 'USE THIS for percentage-based scaling requests like "make 30% wider"'. It also provides a recipe instruction for multi-glyph tasks and explains the fallback behavior. It does not name alternatives or exclusions, so it is not a full 5.

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