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ui_to_artifact

Transform UI screenshots into code, prompts, design specifications, or descriptions. Provide an image, choose an output type, and give detailed instructions to generate the artifact.

Instructions

Convert UI screenshots into various artifacts: code, prompts, design specifications, or descriptions.

Use this tool ONLY when the user wants to:

  • Generate frontend code from UI design (output_type='code')

  • Create AI prompts for UI generation (output_type='prompt')

  • Extract design specifications (output_type='spec')

  • Get natural language description of the UI (output_type='description')

Do NOT use for: screenshots containing text/code to extract, error messages, diagrams, or data visualizations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDetailed instructions describing what to generate from this UI image. Should clearly state the desired output and any specific requirements.
output_typeYesType of output to generate. Options: 'code' (generate frontend code), 'prompt' (generate AI prompt for recreating this UI), 'spec' (generate design specification document), 'description' (natural language description of the UI).
image_sourceYesLocal file path or remote URL to the image
Behavior4/5

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

With no annotations, the description carries the burden of disclosure. It reveals behavioral limitations (unsuitable for text/code extraction, error messages, etc.) and conveys the core transformation behavior. It stops short of detailing output formats, failure modes, or prerequisites, but the provided context is largely sufficient.

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

Conciseness4/5

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

The description is well-organized into a clear opening sentence, a bulleted list of use cases, and an explicit exclusion list. While slightly longer than the minimal viable, every section contributes to effective instruction without redundancy.

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

Completeness5/5

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

The description, combined with the fully described input schema, covers the tool's purpose, valid output types, specific use cases, and content exclusions. The absence of an output schema is offset by the schema's enum descriptions, making the overall description complete enough for an agent to invoke the tool correctly.

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 description coverage is 100%, so the baseline is 3. The description's bullet list largely mirrors the output_type enum values already documented in the schema, adding little new parameter-level meaning. No additional detail is given for image_source or prompt beyond their schema descriptions.

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 clearly states the tool's function: converting UI screenshots into code, prompts, specs, or descriptions. It differentiates itself from sibling tools by explicitly listing excluded content types like text/code extraction, error messages, diagrams, and data visualizations, which correspond to other tools' scopes.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use scenarios for each output_type and lists concrete 'Do NOT use' cases (screenshots with extractable text/code, error messages, diagrams, data visualizations), making tool selection unambiguous for the agent.

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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