Design System Agent Readiness Checker
brought to you by zeroheight

Your documentation was written for humans.

Here’s what happens when an AI reads it.

DS ARC is a tool that helps you test the agent-readability of your design system documentation. We run a sample of your docs against a rubric based on the Design System Documentation Spec, and then point an agent at the docs with eight real jobs. Then we show you the transcripts, and give you real-world advice on how to make your docs more agent-readable.

Provide your public documentation

For documentation that’s already published on the open web. We crawl the URL you give us the same way an agent would.

One run reads up to 40 pages, sampled across your sections rather than crawled in order, and takes about five minutes. Your report link is unguessable, not indexed, and expires in 14 days. There is a limit on how often one site can be checked.

Testing against a solid rubric

One of the questions we get asked a lot is ‘how do we make sure the documentation we’re writing is good for AI agents?’ We strongly believe that good documentation should stand up well for both humans and machines, but there’s possibly still some work to be done for most teams. To start with, we run a sample of your documentation against the conventions outlined in the Design System Documentation Spec, a community-run spec for machine-readability of design system documentation. We mark your docs against 12 guidelines drawn from the spec, to give you an overall ‘machine-readability’ score, as well as improvements you can make tomorrow.

Read more about the rubric →

Testing against a real-world agent

Running a rubric against your documentation is only theory, though. We want to show you what happens when a real AI-agent reads your documentation with queries that mimic real-world application. That’s why we formulate a series of tests based on the documentation we crawl, and run them multiple times against your documentation using Anthropic’s sonnet-4.5 model. Each test is designed to answer a specific area, from token lookups, to ambiguous components, to components that don’t exist. We then show you exactly what worked and what didn’t work, with suggestions on how to fix your documentation to ensure you’re providing the best context possible.

Read more about the battery →

Hand AI agents one tool to give them all the design system context they need.

zeroheight is the documentation platform that brings the whole design system together: Figma, Storybook, tokens and guidelines on one published page, with governance built in. One MCP connection then serves that context to every AI tool you use.

Already on zeroheight? Run DS ARC on your styleguide →

What the agent asked forWhat zeroheight hands it
  • Which component, and when
    Choosing a component · Choosing between two · Look-alike names

    Usage guidance on the component page: when to use it, when not to, and the do-and-don't pairs. Neither Figma's MCP nor Storybook's carries this.

  • What it takes
    Component props · Allowed values

    Stories and code on the same page as the rules, so props and variants arrive with their constraints.

  • Which token and when to use it
    Token lookup

    Token sets that say what each token is for, not just what it is called.

  • How it behaves
    Keyboard interactions

    Accessibility guidance where the component lives.

  • Whether it exists at all
    Invented component

    Published pages only. Drafts and hidden content never reach an agent, so it cannot build from a component that isn't there.

1published sourceThe whole system on one page
Figma and Storybook plug straight in. Designs, code, tokens and guidelines sit together, with inbuilt governance. No more spreading your docs against twenty different tools.
Hoursnot monthsDocumentation to a good standard
Agentic flows draft, audit and fill the gaps in your pages against the standard this report scores, taking the work from months to hours.
10×fewer tokensOne MCP for every agent
Cursor, Claude Code, Copilot, Figma's agent, v0, Lovable and any LLM read the same published context. Enterprise teams see a 10× cut in AI token use against markdown or raw files; Free and Starter see 3×.