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UI To Artifact

ui_to_artifact
Read-only

Generate frontend code, AI prompts, design specifications, or natural-language descriptions from UI screenshots. Provide an image source, output type, and detailed instructions.

Instructions

Convert UI screenshots into artifacts: frontend code, AI prompts, design specifications, or natural-language descriptions.

Use only for UI screenshots. Do not use for OCR-only screenshots, error messages, diagrams, or charts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDetailed instructions describing what to generate from the UI image.
output_typeYesArtifact to generate: code, prompt, spec, or description.
image_detailNoOptional Codex Responses input_image detail. Defaults to CODEX_VISION_IMAGE_DETAIL or high.
image_sourceYesLocal file path, remote URL, or data:image URL to the UI screenshot.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
errorNo
successYes
metadataNo
Behavior3/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context about output types and exclusions, but does not disclose additional behavioral traits like permissions, rate limits, or response details. With annotations present, this is adequate.

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 with no wasted words. The first sentence describes the core action, the second provides clear usage boundaries. Every sentence adds value.

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?

With 4 parameters, 100% schema coverage, annotations, and an output schema, the description is sufficiently complete. It covers purpose, usage boundaries, and output types. Minor improvement could detail image_detail options, but schema already handles that.

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

Parameters4/5

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 semantic context by listing artifact types ('frontend code, AI prompts, design specifications, or natural-language descriptions'), which relates to the output_type parameter, enhancing understanding beyond the schema enum.

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 uses a specific verb-resource pair ('Convert UI screenshots into artifacts') and lists artifact types. It explicitly distinguishes from siblings by stating what not to use it for (OCR-only screenshots, error messages, diagrams, charts), making the purpose clear and differentiated.

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 states 'Use only for UI screenshots' and 'Do not use for OCR-only screenshots, error messages, diagrams, or charts', providing clear when-to and when-not-to-use guidance. However, it does not explicitly name sibling tools as alternatives, which would be more comprehensive.

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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