Framelink Figma MCP vs Kombai: Which is Best for Figma-to-Code Automation?

Turning a Figma design into clean, working frontend code always takes more effort than it should.
You open the file, and suddenly you’re naming layers, adjusting spacing, rewriting components, and fixing weird flex behaviors.

Even when the design system is solid, the translation from design to code is never automatic. It’s still a lot of manual work. That’s the space tools like Figma MCP, and Kombai are trying to fix.

Both aim to bridge the design-to-code gap but in very different ways. Figma MCP gives AI models access to design context inside Figma, so they can interpret layouts and structure.

Kombai focuses on reading those same designs and generating production-ready React or Tailwind code that fits into your existing project.

This post breaks down how both actually work in practice, how they read designs, how the code turns out, and how each fits into a real frontend workflow.

Understanding Figma MCP

When I started exploring Figma MCP, I expected a design-to-code tool. It is not that. MCP stands for Model Context Protocol. It allows an AI model to read a Figma file in a structured format instead of just looking at an image.

Imagine a button design inside Figma. It has a rectangle, a text label, and an auto layout rule. Normally, an AI tool would only see that as pixels. With MCP, the model can access detailed data like the layer type, font style, color value, and position.

This data is shared in a format similar to JSON, which makes it easier for a model to understand what the design actually contains.

MCP itself does not generate any code. It only passes this design information to a connected model. The model decides what to do with it. It can produce React code, check for design consistency, or even document components.

This setup gives us, the developers, the flexibility. You can connect your own AI model, define how the design should be processed, and customize the output. The trade-off is effort. You have to handle the logic, the code generation, and how the output fits into your project. MCP gives you the base, but you have to build on top of it.

How Kombai Interprets and Generates Code

So what is Kombai anyway?

After spending some time with it, I see Kombai as an AI assistant that understands both design and frontend development. It reads a Figma design, analyses its structure, and writes usable code that actually fits into a real project.

When I imported my first design, I expected something basic. What I got was well-structured React code that reflected how the layout was organised in Figma. Kombai detected common patterns like cards, buttons, and navigation bars, then turned them into reusable components with clear naming and proper props.

The output looked like something a developer would write. It used Tailwind classes consistently, kept the hierarchy clean, and avoided unnecessary nesting. I could drop the component into my project, and it worked without major fixes.

Kombai does more than just read Figma files. It adapts to your existing setup. It can look at your project, understand how components are named, and follow your conventions in the generated code. That makes the integration feel smooth instead of disconnected.

The workflow is simple. You select a frame, Kombai analyses it, generates code, and gives you a preview in its sandbox. It also checks for TypeScript and lint errors automatically, which saves time during review.

It still needs developer oversight, especially for responsive layouts or complex logic, but it gets most of the structure right. The best part is that the code it generates feels natural. You can read it, edit it, and extend it without having to clean up a mess.

Prerequisites

Before running the comparison, I used a template that has a hero section, multiple content cards, and call-to-action elements. It’s a balanced layout with enough variety to test how both tools interpret structure, typography, and color handling.

I used the same sections of the design for both tools to keep the test consistent.

For Figma MCP, I connected it to a model that could read design context from Figma and generate React code.

For Kombai, I used the Kombai Cursor extension inside my editor to bring in the same design and generate React and Tailwind components.

Example Comparison

Ok, enough with the setup. It’s time for a proper showdown.

I ran both tools on the entire website design and built out every section to see how far they could go without manual fixes.

Execution with Figma MCP

Here’s how I set up and ran Figma MCP using the Framelink MCP server inside Cursor.

1. Generate a Figma Personal Access Token

1. Open Figma in your browser.
2. Click your profile icon → go to Settings.
3. Scroll down to Security → find Personal access tokens.
4. Click Generate new token.
5. Copy the generated token and keep it safe; you will need it for configuration.

2. Configure the MCP Server in the Cursor

1. Open Cursor.
2. Go to Settings → Tools → MCP.
3. Add a New MCP Server entry.
4. Paste one of the following JSON configurations, depending on your operating system.
5. Replace YOUR-KEY with your Figma personal access token.

MacOS / Linux

{
  "mcpServers": {
    "Framelink MCP for Figma": {
      "command": "npx",
      "args": ["-y", "figma-developer-mcp", "--figma-api-key=YOUR-KEY", "--stdio"]
    }
  }
}

Windows

{
  "mcpServers": {
    "Framelink MCP for Figma": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "figma-developer-mcp", "--figma-api-key=YOUR-KEY", "--stdio"]
    }
  }
}

3. Restart Cursor

After saving the configuration, close and reopen the Cursor to ensure the MCP server is loaded correctly.

4. Verify the MCP Connection

1. Open Agent Mode in Cursor.
2. In Figma, select the frame you want to convert.
3. Right-click the frame → Copy/Paste As → Copy link to selection.
4. Paste the frame link into Agent Mode.
5. If everything is connected correctly, the Cursor should respond to the Figma context or show a confirmation that the frame was read successfully.

5. Generate the Code

Once the connection is verified:

1. Stay in Agent Mode.
2. Paste the Figma frame link again.
3. Ask the agent to generate React + Tailwind code for that frame.
4. Review the generated output. You can save, modify, or export it directly from Cursor.

Here is the outcome:

Execution with Kombai

Kombai was much easier to set up and run compared to MCP. The process is straightforward and doesn’t require any manual configuration.

1. Install Kombai

If this is your first time using Kombai, go to kombai.com and download the extension that matches your editor.
Kombai supports VS Code, Cursor, Windsurf, and Trae.

Once downloaded, install the extension in your editor.

2. Connect Your Figma Account

1. Open the Kombai extension inside your editor.
2. Log in to your Kombai account (or create one if needed).
3. You’ll be prompted to connect your Figma account.
4. Authenticate using your Figma credentials and allow access.

Once this is done, your Figma is now linked to Kombai.

3. Import the Figma Design

1. Open the Kombai extension inside your IDE.
2. In the chat bar, click the Figma option.
3. Go to Figma and select the frame you want to convert.
4. Right-click → Copy/Paste As → Copy link to selection.
5. Paste the copied Figma link into Kombai.

Kombai will automatically start analyzing the design.

4. Generate and View the Output

After you paste the link, Kombai begins converting the design into code.
This usually takes a few seconds to a few minutes, depending on the design’s complexity.

Here is the outcome:

Once done, you can view the generated output inside Kombai Browser (Beta).
It shows a live preview of your design converted into code. You can explore the output and verify how close it is to the original Figma layout.

5. Debug or Refine the Output

If something doesn’t look right, Kombai lets you debug directly in its browser.
You can use the Kombai Agent to fix specific elements:

• Attach an image showing what’s wrong.
• Select the exact UI element using the Reference Selection tool.
• Kombai will adjust or regenerate the code for that part of the design.

It’s a fast way to correct spacing, alignment, or color mismatches without manually editing the generated files.

Outcome Comparison

Both tools were tested on the same Website Template inside Cursor. For Figma MCP, I used the Framelink MCP server setup, which required manual configuration with an API token. For Kombai, I used the Kombai Cursor extension, which was much easier to set up, install, connect to Figma, and start converting designs right away.

The Figma MCP output captured the structure but needed work before it was usable. The layout was correct, yet the details were off. Spacing and alignment required manual adjustments, and image rendering wasn’t consistent. The result looked more like a functional wireframe than a finished UI.

The Kombai output came out much closer to the actual design. It maintained consistent spacing, colors, and typography, and the component hierarchy looked natural. Images displayed correctly, and the generated code was organized enough to use directly with small tweaks.

In short, MCP handled the structure well, but Kombai’s setup and output both felt more practical for real frontend work.

How Kombai Closed the Design-to-Code Gap

After spending time with both tools, the real difference wasn’t just in setup or output. It was in how effortlessly Kombai fit into a real development workflow.

1. Simple Setup, Faster Start

Setting up MCP took time, like generating tokens, configuring the server, and checking connections. Kombai was ready within minutes. Install the extension, connect Figma once, and you’re good to go. No server setup or manual configuration.

2. Clean, Reusable Code

Kombai produced readable, reusable components that followed modern frontend patterns. Props were cleanly defined, Tailwind classes were appropriately used, and the structure matched real-world React projects. MCP’s output was functional but needed refactoring to reach the same quality.

3. Adapts to Existing Codebases

Kombai fits into your existing project without breaking conventions. It recognizes file structure, component naming, and styling patterns. The generated code blends in naturally; no need to rewrite or reorganize to make it work with your setup.

4. Flexible Tech Stack Options

Kombai is not tied to a single framework. You can choose or edit the tech stack before generation. It supports React, Tailwind, and other frontend combinations, and can align with your preferred libraries or design systems.

5. Design Fidelity and Feedback

The output consistently matched the original Figma design, and spacing, typography, and color accuracy stayed intact. The Kombai Browser preview also made debugging easy. You could immediately see issues, highlight an element, and fix them without leaving the environment.

Conclusion/Summary

Working with both tools made it clear that they approach the same problem differently. Figma MCP is a general protocol that allows AI models to access design data, but it is not built specifically for production-level code generation. It gives developers flexibility, but most of the structure and logic still need to be handled manually.

Kombai, on the other hand, is built around the design-to-code workflow as one of its core capabilities. It understands design structure, adapts to existing codebases, and produces clean, readable components that fit into real projects. The setup is simple, the feedback loop is fast, and the output is close to what developers would write themselves.

If you want to see how an AI tool can work with your code instead of just generating it, try Kombai in your editor and run it on your own designs.