Figma MCP vs Figma AI Bridge: What's the Difference

Both Figma MCPs and AI Bridge let your AI read Figma files. They solve different problems. MCPs give you read access to layout, text, and hierarchy. Bridge gives you read-write access to the design token layer underneath.

Here is when to use which.

What Figma MCPs do

A Figma MCP (Model Context Protocol server) exposes your Figma file to an AI model through a standardized interface. The model can traverse the node tree, read component properties, extract text content, and get style values like fills, padding, and font sizes.

Most MCPs — including the official Figma Dev Mode MCP — are read-only. They are good at answering structural questions: what is this component made of, how are its children laid out, what text does it contain.

They are free, open-source, and work with any MCP-compatible model. If you just need an AI to understand what a design looks like, MCPs handle that well.

What they return

When an MCP reads a button component, you get raw computed values:

type: FRAME
name: button
width: 120
height: 32
fills: ['#181818']
borderRadius: 9999
padding: 0 16 0 16
gap: 6
children:
  - type: TEXT
    characters: "Submit"
    fontSize: 14
    fontWeight: 500
    fills: ['#FFFFFF']

Every value is a snapshot. #181818 is a hex color with no semantic meaning. 16 is a pixel count disconnected from your spacing scale. 9999 is a number, not radius/full.

An AI reading this writes background: #181818 into CSS. It works — until someone switches to dark mode, adjusts the density scale, or applies a different theme. Then every hardcoded value needs manual correction.

What AI Bridge does differently

Bridge reads the same node and returns the variable bindings attached to each property:

{
  "fills": "background/primary/solid",
  "paddingLeft": "button/pill-gutter/md",
  "paddingRight": "button/pill-gutter/md",
  "height": "control/size/md",
  "itemSpacing": "button/gap/lg",
  "topLeftRadius": "radius/full",
  "topRightRadius": "radius/full",
  "bottomLeftRadius": "radius/full",
  "bottomRightRadius": "radius/full"
}

Now the AI writes background: var(--color-background-primary-solid) instead of #181818. Dark mode, theming, and density changes are handled by the token layer automatically. The generated CSS references intent, not pixels.

Bridge also writes. It can create frames, text nodes, components, and instances inside Figma — then bind every property to design tokens. This makes a two-way workflow possible: AI prototypes in Figma, binds tokens, reads bindings back, and generates matching code. The Design Tokens, Not Just Pixels post covers this in detail.

Feature comparison

The gap is not about reading structure — both do that. The gap is about the semantic layer. MCPs return what things look like. Bridge returns what things mean.

When standard MCP is enough

If your Figma file does not use variables (design tokens), an MCP gives you everything Bridge would — because there are no token bindings to read. You get raw values either way.

MCPs also work fine when you need an AI to understand layout intent without generating production CSS. Answering "how many items are in this list" or "what text does this card contain" does not require token access.

Common cases where an MCP is sufficient:

• Reading text content from designs for copy review.
• Understanding component hierarchy and layout direction.
• Extracting dimensions for rough prototyping.
• Files without a design token system.

When you need Bridge

Bridge matters when there is a token system and you want the AI to use it. Specifically:

Production CSS with var() references. If your codebase uses CSS custom properties mapped to design tokens, you need the AI to output var(--spacing-lg) instead of 12px. MCPs cannot provide this mapping because they do not read variable bindings.

Dark mode and theming. Hardcoded hex values break across themes. Token references adapt automatically. Bridge returns the token name; the AI uses it directly in code.

Two-way design workflows. Bridge is not read-only. Your AI can create a component in Figma, bind it to the correct tokens, and verify the result — all through the same API. This is useful when the AI is building Figma components alongside code, as described in Connect Claude Code to Figma and Connect Cursor to Figma.

Consistency at scale. When an AI builds 30 components over a week, drift between design and code compounds. Token bindings eliminate drift by construction — the AI references the same source of truth the designer used.

Can you use both?

Yes. They are not mutually exclusive.

You could use a standard MCP for quick structural reads — checking what is on a page, reading text content, understanding hierarchy — and use Bridge when you need token bindings or write access.

Bridge operates through a local HTTP API (http://localhost:8867), not the MCP protocol, so there is no conflict. Both can connect to the same Figma file simultaneously.

The short version

MCPs read the surface. Bridge reads the system underneath. If your workflow stops at "what does this look like," MCPs are free and capable. If your workflow continues to "generate production code that respects the design system," you need the token layer.

Get AI Bridge