Locofy

Locofy's case rests on breadth of workflow rather than a single killer feature. The documentation is explicit that designers, builders, and developers work differently, and each is given a first-class path: the plugin keeps designers inside Figma or Penpot with more control over conversion; the CLI drops into engineering workflows and converts from a Figma URL; the MCP server makes design-to-code usable from AI agents and copilots; and the URL import route converts an existing Figma frame with no plugin installation at all. That last point matters for teams evaluating the tool — you can test the conversion on a real frame before committing anyone's workflow to it. Where Locofy goes beyond simple export is in the structural work: intelligent component detection, responsive layout generation, image asset optimization, and a design system that keeps output consistent. Component mapping lets generated code align with tokens already in your codebase, which is the difference between a demo and something you can merge. Live browser editing and Agent Mode let you correct output in place instead of round-tripping back to the design file. GitHub sync and a VS Code extension cover the developer side, and MCP support means the tool fits the AI-copilot habits many frontend developers now have. The honest weakness is scope. Locofy is optimized for the visual layer of a frontend — structure, layout, components, assets. Teams dealing with complex application state, data fetching, or heavy business logic will treat the output as a scaffold and write the rest by hand. It is also tied to Figma and Penpot: if your design work happens elsewhere, there is no path in. Neither of those is a flaw in the tool so much as a boundary on where it pays off. Within that boundary — MVPs, landing pages, prototypes promoted to real code, design handoff, client work — the time saved on layout and CSS is the reason to use it.