design_from_prompt

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does add useful behavioral context: it analyzes mood and industry, picks design tokens, and composes 30-300+ elements. However, it does not disclose side effects such as where the design is created (new page, current page, selected frame), whether any existing content is replaced, or what the tool returns after generation.

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 tightly structured and front-loaded: the core purpose is stated first, followed by the analysis criteria and output scale. The enumerated moods and industries are useful rather than wasted words. It is thorough without being bloated.

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?

The description gives a strong overview and output scale, but no output schema exists and the description does not explain the return value or document mutation behavior. An agent invoking this tool would benefit from knowing whether it creates a new frame/page, replaces existing content, or returns a list of created node IDs.

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%, so the schema already documents both prompt and width. The description reinforces that prompt is a free-form design description, but adds little beyond the schema's examples and default width. Baseline 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?

States a specific verb, resource, and outcome: 'Takes any natural language description and generates a complete Figma design.' The description further distinguishes the tool from low-level editing siblings by enumerating the design dimensions it handles (mood, industry, colors, spacing, typography). This makes it clear this is a high-level generative tool, not a node-level editor.

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 clearly implies when to use it: when the user has a natural language design request and wants a complete multi-section design. It does not explicitly name alternative tools or exclusion cases, but the context is strong enough for an agent to distinguish it from the many single-purpose siblings like create_frame or set_fill.

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