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create_ui_asset

Generate a saved pixel-art UI panel from a text description. Returns a job ID for tracking and supports custom shapes or predefined UI elements.

Instructions

Generate a shape-based pixel-art UI panel (Pro) from a text description — a persistent, saved UI asset (distinct from generate_ui, which is a one-shot generator). Returns a job_id and a ui_asset_id immediately; poll get_job_status or get_ui_asset until ready. Optionally scaffold the panel from named UI elements or a custom shape template.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFriendly name for the saved asset
seedNoSeed for deterministic generation (default 0)
piecesNoOptional custom shape template. Each piece needs a unique id, a kind, and an optional label. Coords are on a virtual canvas where the longer side spans 0–512 and the shorter side scales to the output aspect ratio. kinds: rounded_rect {x,y,w,h,radius}, circle {x,y,r}, polygon {x,y,r,sides,phase}.
elementsNoOptional named UI element types to scaffold the panel from (auto-positioned, no coords needed). Combine with pieces for custom shapes; omit both for a default full-canvas panel.
image_sizeNoOutput size in pixels, 192–688 per axis (max per axis depends on aspect; default 256×256)
project_idNoIf set, assign the finished asset to this project
descriptionYesStyle description for the UI panel (e.g. 'wooden RPG panel with gold trim')
style_imageNoOptional style reference image (PNG/JPEG)
color_paletteNoOptional palette specification (e.g. 'brown and gold')
no_backgroundNoGenerate with transparent background
Behavior3/5

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

No annotations provided, so description carries full burden. It correctly discloses the async behavior (returns IDs immediately, poll status) and notes it's a 'Pro' feature. However, it lacks details on error handling, rate limits, or authorization.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences convey purpose, distinction, return flow, and customization. No wasted words. Front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 10 params, no output schema, and nested objects, the description covers workflow (generate, poll), distinction from generate_ui, and optional scaffolding. Missing error handling details but adequate for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds high-level context for key parameters (description, elements, pieces) but doesn't elaborate on all 10 params. It meets the baseline by not repeating schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it generates a persistent, saved UI asset from a text description, using specific verbs and resources. It clearly distinguishes from generate_ui (one-shot generator).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description directly compares with generate_ui, indicating when to use this tool for persistent assets. It also outlines the polling workflow, providing context for usage. No explicit exclusions, but clear guidance overall.

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

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