Introduction

Design-to-code AI tools have been gaining momentum, and when Figma released its MCP (Model Context Protocol) integration in beta, I decided to give it a try. At first, I was sceptical — a part of me wondered if my role as a developer might get automated away.

But curiosity won. I integrated the MCP with VS Code and began testing how it converted Figma designs into code. What followed was a mix of small wins, mild frustrations, and a much clearer understanding of where AI fits into my workflow.

Getting It Up and Running

Integration wasn’t as seamless as I expected. VS Code didn’t always recognise the MCP extension, especially when I had multiple projects open. Copilot didn’t help much either. Eventually, I got it working, and I began using its “Get Code” feature to generate code from selections in Figma.

This process required jumping back and forth between Figma and VS Code — and while it worked, the results were… mixed.

What I Found Out

1. Productivity Boost — But Not a Free Ride

When I tried generating an entire page, the tool only recognised certain components and asked for my confirmation before generating code. Generating single components worked better, and I could choose between HTML/CSS or frameworks like React, Vue, or Angular.

Here’s the surprising part: even though I’m primarily a React developer, I could suddenly produce Vue code without any prior experience — because the AI handled the syntax. That’s a genuine productivity gain.

But after getting the code, there’s still a lot to do:

Validate the structure to make sure it’s semantic and accessible
Fix CSS so it actually matches the design (radio buttons were always images, and spacing was often off)
Integrate logic and wire it into the application like a puzzle piece
Ensure naming conventions and class structures are clean and maintainable
Check for coding standards and accessibility compliance

All told, I’d say I still do about 80% of the work — AI just gives me a solid head start.

2. Accuracy: Good Outline, Flawed Details

The generated code usually captured the layout and text content well. However, visual fidelity wasn’t perfect — the rendered components sometimes looked noticeably different from the original design.

That means AI isn’t replacing the careful, detail-oriented work developers do; it’s just reducing the time spent on repetitive boilerplate coding.

3. Security Awareness

Although my testing didn’t focus on security, using any AI tool that accesses your codebase requires caution. API keys, auth tokens, and other sensitive data should always be protected, even if the tool promises your data is safe.

Conclusion

After weeks of testing, I realised that AI-assisted design-to-code tools aren’t here to replace developers — they’re here to change how we work.

The AI can give me a boilerplate, help me code in unfamiliar frameworks, and speed up repetitive tasks. But validating, refining, integrating, and securing the final product? That’s still on me.

For now, AI is a powerful assistant — but the craft, judgement, and responsibility still belong to the developer.

My Experience Integrating AI Design-to-Code Tools: Productivity, Accuracy, and Reality Check