create_sampler

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

No annotations, so the description carries the full behavioral burden. It adds useful artifact detail (identical controls/widths/heights, raised numerals, left-edge notches, no Braille needed), but discloses nothing about side effects, whether files are written, or overwrite semantics—significant gaps for a creation tool with six parameters.

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?

Front-loaded with the goal in the first clause, then parameter hints, then the follow-up pointer. Densish but each clause contributes; little waste.

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?

An output schema exists and the description wisely redirects return-value questions to describe_model, so that burden is met. However, with no annotations, 0% param coverage, and four undocumented parameters, the definition is only partially complete for a six-parameter creation tool.

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 0%, so the description must compensate. It explains profile (default active) and focus (row type: profiles/widths/heights/spacing), but leaves name, seed, printer, and overwrite entirely undocumented—only two of six parameters get semantic meaning.

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?

States a specific verb and artifact: 'Build a calibration plate to find which differences the user can feel', which clearly distinguishes it from create_model (which presumably builds a normal model). The purpose is concrete, though it stops short of explicitly contrasting the sibling create_model.

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?

Describes the scenario (feeling out perceptible differences) and routes the follow-up step to describe_model for a readout, but gives no when-not guidance or explicit alternative to create_model. Usage is implied rather than spelled out.

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