About tldraw
The tldraw MCP app exposes an interactive tldraw canvas to AI agents through the MCP app specification. It is aimed at users who want to work on diagrams with agents in any MCP client that supports that spec. It runs on Cloudflare Workers and includes local development and tunnel workflows.
Key features
- 01Interactive tldraw canvas available to agents
- 02Search tool for the Editor API specification
- 03Exec tool that runs JavaScript on the canvas
- 04Checkpoint storage in Durable Objects with SQLite
- 05Connection options for Claude Desktop and ChatGPT
Use cases
- 01Letting an AI agent draw diagrams on a whiteboard
- 02Editing a canvas together with an agent in an MCP client
- 03Debugging locally through a tunnel in ChatGPT
MCP support
This project is an MCP server that exposes search and exec tools to MCP clients, along with app-only tools used by its widget to persist canvas checkpoints. It works with the MCP app specification for rendering the canvas inside the host.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://tldraw-mcp-app.tldraw.workers.dev/mcpclaude mcp add --transport http tldraw https://tldraw-mcp-app.tldraw.workers.dev/mcp{"mcpServers":{"tldraw":{"type":"http","url":"https://tldraw-mcp-app.tldraw.workers.dev/mcp"}}}Open in Cursor{"servers":{"tldraw":{"type":"http","url":"https://tldraw-mcp-app.tldraw.workers.dev/mcp"}}}Open in VS Code{"mcpServers":{"tldraw":{"type":"http","url":"https://tldraw-mcp-app.tldraw.workers.dev/mcp"}}}{"mcpServers":{"tldraw":{"serverUrl":"https://tldraw-mcp-app.tldraw.workers.dev/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://tldraw-mcp-app.tldraw.workers.dev/mcphttps://tldraw-mcp-app.tldraw.workers.dev/sseTools
6| Parameter | Type | Required | Description |
|---|---|---|---|
| code | string | Yes | JavaScript code that receives `spec` and returns a result. Must use `return` to produce output. |
Details
From the project READMEtldraw MCP app
This is the tldraw MCP app. It exposes an interactive tldraw canvas to AI agents via the Model Context Protocol app specification, so you can work in tldraw with agents in any MCP client that supports the MCP app spec.
Architecture
The app has two parts: a server and a widget.
Server
The server runs in Cloudflare Workers via src/worker.ts, using a Durable Object (TldrawMCP) backed by SQLite for persistent checkpoint storage.
It exposes:
search— query the extracted Editor API spec in a sandboxed dynamic workerexec— execute JavaScript against the live editor in the widget via a pending-request callback bridge_exec_callback— app-only tool the widget calls to resolve a pendingexecrequestsave_checkpoint/read_checkpoint— app-only tools used by the widget for checkpoint persistence
Widget
The widget is a React app (src/widget/mcp-app.tsx) that renders a full tldraw canvas inside the MCP host's iframe.
When the AI calls exec, the server creates a pending request and the widget picks it up, runs the code through a focused editor proxy (src/widget/focused/) that translates between an AI-friendly shape format (simple string IDs, flat _type shapes) and tldraw's internal TLShape/TLShapeId types, then calls _exec_callback to resolve the pending request with the result. Canvas state is checkpointed to the Durable Object's SQLite database and to the browser's local storage.
Developing
Prerequisites
The widget build depends on generated files (editor-api.json, method-map.json) that are extracted from the editor's TypeScript declarations. Before you can develop or build the mcp-app, you need to build the core packages first:
# from the repo root
pnpm build
This produces the .tsbuild/ output that pnpm extract-api reads from. The build and dev scripts run extract-api automatically, so you don't need to call it separately.
Package scripts
Run all commands from apps/mcp-app.
| Command | What it does |
|---|---|
pnpm build | Build the widget HTML |
pnpm dev | Build widget + start local Cloudflare worker (HTTP MCP on localhost:8787) |
pnpm dev:tunnel | Build widget + start a stable named Cloudflare tunnel + local worker with WORKER_ORIGIN set to the tunnel URL |
pnpm run deploy | Build widget + deploy the Cloudflare worker to production |
pnpm dev:tunnel requires the cloudflared CLI to be installed on your machine and a one-time cloudflared tunnel login. It serves a stable per-user hostname (<user>-mcp-app-dev.tldraw.xyz) via a named tunnel, so the URL you register in hosted MCP clients stays the same across runs instead of changing every time. The user slug defaults to whoami; override it (or the zone/port) with MCP_TUNNEL_USER, MCP_TUNNEL_ZONE, and PORT. The zone must be a Cloudflare-hosted zone (tldraw.xyz is; tldraw.dev/tldraw.com are on Vercel and won't work).
The worker defaults to production-safe behavior in wrangler.toml, including setting MCP_IS_DEV="false". Local HTTP dev scripts override that with MCP_IS_DEV=true so local Claude/ChatGPT connectors suppress ui.domain while production deployments keep it enabled.
Cursor setup
Add these two servers in ~/.cursor/mcp.json:
{
"mcpServers": {
"tldraw": {
"transport": "http",
"url": "https://tldraw-mcp-app.tldraw.workers.dev/mcp"
},
"tldraw-local": {
"command": "npx",
"args": ["-y", "mcp-remote", "http://127.0.0.1:8787/mcp"]
}
}
}
Claude Desktop local setup
For local Claude Desktop development, use claude_desktop_config.json with the local HTTP server:
{
"mcpServers": {
"tldraw-local": {
"command": "npx",
"args": ["-y", "mcp-remote", "http://127.0.0.1:8787/mcp"]
}
}
}
Claude Desktop remote setup
If you'd like to try the remote MCP server in Claude Desktop, use the in-app connector flow rather than adding the production URL to claude_desktop_config.json.
- Open Claude Desktop
- In the sidebar, go to Customize
- Open Connectors
- Click the button to add a connector, then choose Add custom connector
- Give it a name such as
tldraw - Paste
https://tldraw-mcp-app.tldraw.workers.dev/mcpas the server URL
The Add custom connector option is not available on the free plan, so you may need Max or another paid plan.
If you need Notion access in Claude Desktop, use the Notion MCP connector for that separately.
ChatGPT local dev
ChatGPT requires an HTTPS origin, so you need a Cloudflare tunnel. You must be an admin of your OpenAI org/workspace to do local dev.
First-time setup (once per machine):
- Run
cloudflared tunnel loginand choose thetldraw.xyzzone
Then, each session:
- Run
pnpm dev:tunnelinapps/mcp-app - It serves your stable hostname, e.g.
https://<user>-mcp-app-dev.tldraw.xyz(the first run also creates the named tunnel and its DNS route) - In ChatGPT web (not the desktop app), go to Apps and add your app using that URL
- You can then test in both ChatGPT web and the desktop or mobile apps
Because the hostname is stable per user, you only add the app in ChatGPT once — subsequent pnpm dev:tunnel runs reuse the same URL.
dev:tunnel automatically wires WORKER_ORIGIN to the stable tunnel URL and sets MCP_IS_DEV=true for the local worker.
Iteration loop
- Make code changes in
apps/mcp-app - Run the relevant script (
devordev:tunnel) - Disconnect and reconnect the MCP server in your client (or reload the page/app)
- When making widget changes, make sure to rebuild, either by running
pnpm buildor rerunning any of the dev scripts.
Reconnecting the server after changes is the most reliable way to pick up new code, especially when the widget HTML changes.
Session storage lifecycle
Each MCP session is a TldrawMCP Durable Object. It keeps up to MAX_CHECKPOINTS (50) canvas snapshots and destroys itself after IDLE_TTL_MS (7 days) without a checkpoint save — a schedule armed in init() and re-armed on every check.
Contact
Find us on Twitter/X at @tldraw.
Community
Have questions, comments or feedback? Join our discord. For the latest news and release notes, visit tldraw.dev.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.