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kolganovr

figma-mcp-bridge

⚡ Figma MCP Bridge

Let an AI agent design in Figma — and actually see what it drew.

A zero-dependency MCP server that gives coding agents read and write access to a live Figma canvas, then closes the loop by handing the rendered PNG back to the model for visual self-critique.

License: MIT MCP Node.js Dependencies Tools Works on


Figma MCP Bridge Demo

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.


Related MCP server: figma-mcp-lightweight

How it's different

This bridge

Figma Dev Mode MCP (official)

Framelink

Talk to Figma

Writes to the canvas

✅ arbitrary JS in the sandbox

✅ code-to-canvas

❌ read-only

✅ fixed command set

Visual feedback loop

✅ auto-framed PNG on every write

⚠️ separate screenshot call

Undo the agent's work

figma_rollback

n/a

Works on the Figma Free plan

❌ Dev/Full seat, paid plan¹

Tool-call quota

none — it's local

6 / month on Starter seats¹

inherits REST rate limits

none

Token-optimized read of the live doc

✅ 86–91% smaller²

⚠️ partial

❌ REST only

Persistent code modules in-sandbox

bridge.define/require

Long jobs survive their own timeout

✅ async job_id + progress

n/a

Install footprint

0 npm deps, npx or git clone, Node only

Figma desktop + paid seat

npx + access token

Bun + a second server process

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

The live canvas tools need no token. If you also want to read unopened cloud files (get_file, get_node, get_styles, …), supply a personal access token:

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

Without a token these 7 tools aren't registered at all — see Tool reference.


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:

Tier

Count

Registered when

Core

8

always

Extended

5

always

REST

7

only with FIGMA_PERSONAL_ACCESS_TOKEN set

Legacy

3

only with FIGMA_MCP_LEGACY_TOOLS=1

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.

Core — live canvas

Tool

Description

figma_execute_code

Run JS in the Figma sandbox. Injects figma, ensureFont, getFreePosition, progress, bridge. Supports capture, capture_node_ids, diff, async, target.

figma_read_canvas

Token-optimized read of the live document (jsx / tree / json) with budget_tokens.

figma_screenshot

PNG of specific node_ids or the current selection.

figma_find_components

Cached, tokenized, fuzzy component search — variants, properties, keys.

figma_insert_component_instance

Instantiate a component/variant, apply text overrides, place into AutoLayout.

figma_insert_svg

Insert raw SVG with proportional scaling, recoloring, optional component wrapping.

figma_get_variables

Variable collections, modes, and token values.

figma_rollback

Undo a previous write call's checkpoint.

Extended — live canvas

Tool

Description

figma_get_selection

Geometry, compact hex fills, parent/page, AutoLayout context of the selection.

figma_get_canvas_layout

Artboard bounds + a safe suggestedNextPosition. layout:"grid" shelf-packs.

figma_set_variables_mode

Switch theme mode (Dark/Light/Brand) on a frame or page.

figma_job_status

Poll an escalated background job.

figma_list_targets

List connected Figma documents for multi-file targeting.

REST — Figma Cloud (needs a token)

Tool

Description

get_file / get_node

Token-optimized cloud file/subtree. Supports budget_tokens.

get_image

Render nodes to PNG/SVG/PDF via Figma's renderer.

get_styles / get_components

Published styles and design-system components.

get_comments / post_comment

Read and post file comments.

Legacy — opt-in via FIGMA_MCP_LEGACY_TOOLS=1

figma_create_ui_card · get_me · get_image_fills


Engineering notes

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

The obvious way to persist helpers between calls is "stash the source, eval it next time." It fails silently: eval is a bound function in Figma's sandbox, which by spec makes every call an indirect eval — declarations inside it reach neither the caller's scope nor globalThis. Nothing is defined, nothing throws.

bridge.define is built on new Function instead, whose bodies are ordinary function scopes. The runtime's bridge.info() reports this contract to the agent on request, so it can ask instead of guessing.

Zero npm dependencies isn't a vanity metric here — it means git clone && node install.mjs works on a locked-down machine with no registry access, and there's no supply chain to audit for something that executes arbitrary JS inside your design files.

The cost is owning the framing: masking, fragmented continuation frames (a 4 MB screenshot arrives split, and treating each fragment as a whole message silently dropped it until the tool call timed out 40s later), ping/pong liveness, and a 64 MB ceiling so one frame can't exhaust memory.

Figma caps pluginData entries at roughly 100 KB — measured in bytes. The original chunker sliced by JS string length, so a module written in Cyrillic (~2 bytes/char) produced "60,000-char" chunks that were really 120 KB, and setPluginData threw. The splitter now walks the real UTF-8 budget and never tears a surrogate pair.

Every agent spawns its own copy of the server; the first to bind :8765 owns the plugin socket and the rest proxy to it. When the owner exits, a proxy has to be able to take over — otherwise every surviving agent stays permanently broken until restarted. A 5-second watchdog retries the bind, and a failed proxy call triggers an immediate takeover attempt.

The original collision engine rescanned every top-level node for each of up to 200 candidate positions, then did it again in a second function in the same tick — and only ever stepped along one axis, so 20 generated screens became a mile-long ribbon nobody could zoom out to see.

Bounds are now computed once and shared; collisions go through a 500px grid hash; and layout:"grid" shelf-packs into a compact rectangle.

Raw Plugin API errors are famously unhelpful. Failures are matched against known modes and rewritten with a HINT: line naming the API that actually works, plus a stable machine-readable code (FONT_NOT_LOADED, INSTANCE_TRANSFORM_LOCKED, STALE_NODE_ID, AUTOLAYOUT_HUG_RESIZE, AMBIGUOUS_TARGET, …) so agents and tooling can branch on the failure type without parsing prose.

:8765 is loopback, but loopback is reachable by any web page the user happens to have open. Two independent gates: an Origin allowlist (a browser cannot forge Origin, so a page on evil.com is rejected at the handshake) and a shared token that install.mjs generates and bakes into both the MCP config and the installed plugin — which also covers the sandboxed-iframe case, where a hostile page can present Origin: "null" too.

Running straight from a clone with no token, the Origin gate still applies and the server prints a warning, so it degrades rather than silently opening up.

On startup the server compares the commit it was installed from (recorded by install.mjs into a version.json beside the copied code) against the latest commit on main via one GitHub API request, and prints a one-line notice to stderr if they differ. It never applies anything itself — node install.mjs --update is still a manual, deliberate step.

This is the one thing in this project that assumes network access, so it's built to disappear cleanly when there isn't any: the check is fired without being awaited (never delays initialize or the first tool call), a failed/slow/offline request is caught and silently skipped, and results are cached for 24h so it doesn't hit GitHub's unauthenticated rate limit or run once per spawned copy of the server. Set FIGMA_MCP_NO_UPDATE_CHECK=1 to turn it off entirely — worth doing on the locked-down machines the previous note is about.


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

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