MCP: Token Aware AI Workflows for Scalable UI

Problem

Design system velocity was capped by hand-authored component scaffolding. Even with a token system in place, scaling across brands still required manual setup, duplication, and human oversight. We needed a smarter way to generate consistent, compliant components at speed.

Solution

We built a token-aware AI workflow that turned prompts into production-ready UI. By feeding structured design token context through MCP to AI tools like Cursor and ChatGPT, we enabled consistent, theme-compliant component generation that could be previewed in context and shipped instantly.

Process

• Generated prompts using MCP-powered token context (brand, mode, state)
• Used ChatGPT + Cursor to scaffold Input, Button, and Modal components with live tokens
• Applied CSS custom properties from Style Dictionary output, ensuring variable compliance
• Embedded components in Storybook with theme switching and preview support
• Synced Figma with REST-based variable automation for visual parity

Results

• Reduced manual component scaffolding time by over 70%, allowing designers and engineers to generate production-ready components with minimal input.
• Standardized token compliance across all generated components, eliminating styling overrides, reducing QA churn, and maintaining full alignment with brand and theme specifications.
• Increased adoption of design system tooling by making component generation accessible to developers and designers who previously relied on system leads or legacy libraries.
• Validated design-code parity using REST-driven automation to keep Figma Variables and production codebases synchronized—ensuring what’s generated is exactly what gets implemented.
• Proved the viability of AI-assisted UX tooling in a production environment by combining GPT-based workflows with token enforcement and accessibility standards.
• Positioned the design systems team as a strategic R&D partner, not just a support function—demonstrating how governed AI workflows can increase velocity without sacrificing quality or consistency.
• Established a repeatable framework for AI-driven component creation, paving the way for future integrations into developer onboarding tools, design system portals, or design ops pipelines.