figma-mcp-bridge

Agent generates the design, visually audits the rendered output, detects layout defects, and self-corrects in real time — zero human in the loop.

The problem

Coding agents are effectively blind inside Figma. Most Figma MCP servers are read-only — they flatten a design into text so a model can turn it into code, and the traffic stops there. The ones that can write are either gated behind a paid Figma seat with a monthly tool-call quota, or expose a fixed vocabulary of commands (create_rectangle, set_fill) that runs out the moment the task gets specific.

And none of them answer the question that actually matters after a write: did it look right? An agent that draws a card, gets back {"ok": true}, and moves on has no way to notice that its text overflowed, its contrast failed, or its frame landed on top of someone else's work.

This bridge closes that loop, and adds the safety rails an autonomous agent needs to be trusted with a real design file.

How it's different

The short version: Framelink is the best choice if you only want to turn an existing design into code. The official server is the safest choice if you're already on a paid Figma plan and want Code Connect. This bridge is for the case where the agent is doing the designing — where it needs to write freely, check its own work, and be undoable when it gets it wrong.

How it works

flowchart LR
    subgraph AI ["🤖 Coding Agent"]
        LLM["Claude Code · Cursor<br/>Antigravity · Windsurf"]
    end

    subgraph Bridge ["⚡ MCP Server — Node.js, 0 deps"]
        Router["Tool Router<br/>+ Job Ledger<br/>+ Target Router"]
        Opt["Token Optimizer<br/>REST + LIVE"]
    end

    subgraph Figma ["🎨 Figma Desktop"]
        Plugin["Bridge Plugin<br/>Checkpoint Journal<br/>Component Index"]
        Canvas["Live Canvas"]
    end

    LLM -->|"stdio JSON-RPC"| Router
    Router <-->|"WebSocket :8765"| Plugin
    Plugin -->|"execute in sandbox"| Canvas
    Canvas -->|"export PNG"| Plugin
    Plugin -->|"raw node tree"| Opt
    Opt -.->|"86% smaller"| LLM
    Plugin ==>|"screenshot + warnings"| LLM

The agent speaks plain MCP over stdio. The server owns a hand-rolled RFC 6455 WebSocket on :8765, which the Figma plugin connects to — so commands reach the canvas with no polling latency, and results (including multi-megabyte base64 PNGs) stream straight back.

Quickstart

npx @kolganovr/figma-mcp-bridge

Or, to read the installer before it touches your machine — it's the same script either way, just fetched differently:

git clone https://github.com/kolganovr/figma-mcp-bridge.git
cd figma-mcp-bridge
node install.mjs

Both copy the server and plugin into place and register the MCP server in every AI client config they find — Claude Desktop, Claude Code, Cursor, Windsurf, Antigravity. The npm package exists purely for a shorter first command; it doesn't add a single runtime dependency — dependencies is empty in its package.json too, and no Python is needed either. node is the only runtime this project ever needs, for the installer and the server alike.

Then, in Figma Desktop:

1. Plugins → Development → Import plugin from manifest… → pick figma-plugin/manifest.json
2. Press Ctrl + Alt + P (macOS: Cmd + Option + P) to launch Antigravity Bridge
3. Status turns green — CONNECTED

Restart your AI client so it picks up the new tools. Verify anytime with:

npx @kolganovr/figma-mcp-bridge --doctor   # or: node install.mjs --doctor

npx @kolganovr/figma-mcp-bridge --token "your_figma_personal_access_token"
# or: node install.mjs --token "your_figma_personal_access_token"

What it gives the agent

1. Write freely, then look at the result

capture: true returns a PNG of exactly what the call just created or modified — auto-framed to the changed nodes, never the whole page, and never by hijacking the user's selection.

// agent calls figma_execute_code
{ "code": "const f = figma.createFrame(); /* ... */ return f.id;", "capture": true }

// agent gets back — text + image in one response
{
  "ok": true,
  "created":  ["12:34"],
  "warnings": ["Text \"Total\" has ~3.1:1 contrast against its parent fill (WCAG AA wants 4.5:1)."],
  "checkpoint_id": "cp_mfk3p2a_7",
  "duration_ms": 840
}

The warnings array is a cheap auto-lint over the touched subtree — text overflow, low contrast, zero-size nodes. It catches the obvious mistakes without spending a screenshot round trip.

2. Undo anything the agent did

Every write opens a checkpoint automatically. One call reverses it — no Ctrl+Z rolling back over the human's own unrelated work.

// agent calls figma_rollback
{ "checkpoint_id": "last" }

// gets back
{
  "ok": true,
  "checkpoint_id": "cp_mfk3p2a_7",
  "label": "Generate checkout flow",
  "removed":  ["12:34", "12:35"],   // nodes the call created — deleted
  "restored": ["9:11"],             // nodes it modified — properties put back
  "missing":  []                    // ids that no longer exist
}

Created nodes are tracked automatically via a Proxy around figma.create*(). Property edits are tracked when snapshotted. Deletions are honestly reported as unrecoverable rather than silently lost.

3. Read the live canvas for ~700 tokens instead of ~5,000

The same pruning + Pseudo-JSX pipeline that powers the cloud tools, pointed at whatever is open right now. Measured on a realistic 6-card layout: 20 KB of raw API JSON → 2.7 KB (86% smaller).

<Frame id="1:1" name="Landing" w="1440" h="900" row gap="20">
  <Frame id="2:0" name="Card" w="300" h="200" col gap="12" pad="20" bg="#FFFFFF" radius="16">
    <Icon id="3:0" name="ic_check" size="24" strokeWidth="2" />
    <Text id="4:0" color="#1A1A1F" font="Inter 18px">Feature 0</Text>
  </Frame>
</Frame>

budget_tokens caps the response: if the requested depth overshoots, the server re-serializes the already-fetched tree shallower — no second round trip — and appends a comment saying what it did and which id to fetch for more.

4. Teach the sandbox new tricks that survive restarts

Every figma_execute_code call is a fresh function scope, so helpers normally die instantly. bridge.define compiles and stores a module inside the .fig document:

// once
bridge.define("kit", `
  async function label(parent, text) { /* ... */ }
  module.exports = { label };
`);

// in any later call — including next week, after a Figma restart
const { label } = bridge.require("kit");

5. Long jobs that don't die at the timeout

A generation still running after 30s hands back a job_id instead of failing while the plugin keeps working. Poll figma_job_status for live progress — the sandbox reports it via progress(step, of, note).

Tool reference

Tools are served in tiers, so the schema list sent to the model on every turn stays proportional to what's actually usable:

Without a REST token an agent sees 13 tools instead of 23 — and never wastes a call on something that would only return REST_TOKEN_MISSING.

Engineering notes

The parts that were harder than they look — and why the code is shaped the way it is.

Testing

Five dependency-free suites, all runnable with bare node:

node tests/bridge-runtime.test.js   # sandbox runtime, module persistence, checkpoint/rollback
node tests/layout-packer.test.js    # row/grid packing, collision grid
node tests/optimizer.test.js        # jsx/tree/json serialization, budget truncation
node tests/mcp-protocol.test.js     # real server over stdio: initialize, tools/list, tiering
node tests/install.test.js          # config merge/reuse, token persistence, stale-file cleanup

mcp-protocol.test.js spawns the actual server as a child process and speaks NDJSON to it — the same transport a real MCP client uses — rather than importing internals.

Repository layout

figma-mcp-bridge/
├── figma/                    # MCP server (Node.js, stdio + WebSocket)
│   ├── index.js              # protocol, tool router, job ledger, target router
│   ├── optimizer/            # AST pruner, style collapser, JSX/tree serializers
│   ├── instructions.md       # agent-facing protocol docs (served on `initialize`)
│   └── *.json                # per-tool schemas
├── figma-plugin/             # Figma Desktop plugin
│   ├── code.js               # sandbox executor, bridge runtime, checkpoints, capture
│   └── ui.html               # HUD — stream, settings, control (pause / undo)
├── tests/                    # 5 suites, 0 dependencies
├── install.mjs               # cross-platform installer, updater, doctor (Node only)
└── AGENTS.md                 # onboarding protocol for AI agents

Sources

1. Seat and quota requirements for the official server — Figma: Guide to the Figma MCP server, Figma Developer Docs: Rate limits & access
2. Token reduction measured on a 6-card layout fixture that mimics real REST output: 20,587 B raw → 2,862 B Pseudo-JSX (86.1%) / 1,912 B tree (90.7%). The fixture is checked in and asserted — run node tests/optimizer.test.js to reproduce the exact figures.

Comparison reflects publicly documented behaviour as of August 2026. Alternatives are actively developed — verify current capabilities before making a decision on this table alone.

License

MIT · Built by Roman Kolganov