generate_mask

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

The description goes beyond annotations by detailing the deterministic and idempotent behavior (same params produce byte-identical PNG and return existing revision). It also clarifies what the mask represents (white = full application, black = none) and that gradients use dimensions while ranges use pixels. The idempotentHint annotation is confirmed and expanded with specifics.

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 moderately long but every sentence serves a purpose: it defines the function, parameter kinds, output semantics, usage example, and idempotency. The front-loaded summary of kinds is efficient, and the JSON usage example is compact yet informative. No fluff or repetition.

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 the tool's complexity (13 parameters, 4 kinds) and presence of output schema, the description covers key aspects: output semantics, idempotency, and usage integration. It omits explicit return value format (though output schema exists) and some edge cases, but the examples and schema descriptions cover most. The description is sufficient for an agent to call it correctly.

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% with detailed descriptions for each parameter including default values and units. The description adds clarity by grouping parameters by kind and explaining the common semantics (like feather behavior) but does not add substantial new information beyond the schema. The description's parameter list matches the schema and provides complementary context (e.g., which parameters apply to which kind), but this is largely redundant.

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: generating a deterministic grayscale mask as a new PNG revision with exact dimensions from a reference. It enumerates four distinct mask kinds with their parametersholistic details, and explains the white/black semantics. This distinguishes it from siblings like render_preview or apply_transform by focusing on mask generation.

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 explicitly explains how to use the result: referencing the returned revision_id in tone/filter operations with a concrete JSON example, and how to visualize it with render_preview overlay. It does not explicitly mention when not to use it, but the detailed operation contracts and idempotency note provide clear context for typical use cases. The alternatives are implied by sibling tools, and the usage examples are sufficient.

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