MindDesigner (tdmcp) — TouchDesigner MCP server
tdmcp is a Model Context Protocol (MCP) server
for TouchDesigner — build TouchDesigner from plain
language. You describe a visual to an AI assistant (Claude, Claude Code, Cursor,
Codex); the AI builds the actual network of nodes inside your project, checks it
for errors, and shows you a preview.
“Create a feedback tunnel from noise with blur and displace, then add bloom and
output it to a window.”
…and the nodes appear, wired up, in your /project1.
It works because it pairs two things every other tool was missing:
- Real knowledge — an embedded reference of 629 operators, 68 Python classes,
workflow patterns, GLSL techniques and tutorials, so the AI uses real
TouchDesigner operators instead of guessing.
- Real execution — a small bridge running inside TouchDesigner that
actually creates, connects, inspects and previews nodes — with a
create → verify → preview loop so the AI can see and fix its own work. Every
generated network is auto-arranged into a readable left→right layout.
📖 Documentation
Full guides and reference live on the docs site → https://pantani.github.io/tdmcp/
🇧🇷 Portuguese documentation: https://pantani.github.io/tdmcp/pt/
How it works
Three pieces talk to each other on your computer:
You + your AI tdmcp server TouchDesigner
(Claude / Cursor) ─▶ (a small program) ─▶ (the bridge inside TD)
"make a feedback builds real nodes
tunnel from noise" in /project1
- Your AI assistant — where you type what you want.
- The tdmcp server — a small Node program that gives the AI a set of
TouchDesigner “tools” and the operator knowledge base. You install it once.
- The bridge — a tiny piece that runs inside TouchDesigner so the server
can actually drive it. You switch it on once per machine.
What you’ll need
- TouchDesigner — the free non-commercial
edition is fine.
- An MCP-capable AI assistant: Claude Desktop (easiest), Claude Code,
Codex, or Cursor.
Node.js is only needed for the build-from-source path (Node 20+).
The one-click Claude Desktop extension needs nothing extra — the server is bundled
inside the .mcpb extension file.
Get started
You set up two sides: your AI (so it gets the tdmcp tools) and
TouchDesigner (so the AI can drive it).
🤖 Easiest — let your AI install it. Using Claude Code, Codex, or
Cursor? Paste this one message in:
Install and connect tdmcp for me using the official install guide:
https://pantani.github.io/tdmcp/guide/install
Do every step yourself; only stop when you need me to do the TouchDesigner bridge step.
It clones, builds and wires everything up; the only manual step is pasting one
line into TouchDesigner (Step 2 below).
🟢 Claude Desktop — one-click .mcpb (no terminal, no Node). Download
tdmcp.mcpb,
then in Claude Desktop open Settings → Extensions and install it (drag it in or
Install from file). Leave host/port at 127.0.0.1 / 9980. Full walkthrough:
the install guide.
🛠️ Claude Code / Codex / Cursor — build from source.
git clone https://github.com/Pantani/tdmcp.git
cd tdmcp
npm run setup # installs, builds, and prints the exact line to connect your client
Turn on the bridge inside TouchDesigner (everyone)
Easiest — no Textport. Download
tdmcp_bridge_package.tox
from the latest release, drag it into your /project1 network, and click
Install on the component. The package self-bootstraps and starts the bridge on
port 9980. ✅
Prefer a one-line Textport command?
Open the Textport (Dialogs → Textport and DATs), paste this one line and
press Enter:
import urllib.request; exec(urllib.request.urlopen("https://github.com/Pantani/tdmcp/raw/v0.13.2/td/bootstrap.py").read().decode())
You should see [tdmcp] bridge running on port 9980 (/project1/tdmcp_bridge).
Either way it’s safe and reversible — it adds one tidy component; remove it later
with from mcp import install; install.uninstall(). Other install methods (module
path, terminal, Palette package) are in the
bridge docs.
Make something
With TouchDesigner open and your AI connected, ask in plain language:
“Create an audio-reactive particle galaxy and show me a preview.”
The AI builds the network, checks it for errors, and returns a thumbnail. Iterate:
“make it warmer,” “add a feedback trail,” “output it fullscreen.” More ideas in
the prompt cookbook.
Not connecting? The two most common fixes: make sure the bridge is on
(curl http://127.0.0.1:9980/api/info returns JSON), and restart your AI
client after adding the server. Full
troubleshooting.
What you can do
509 tools across three layers, plus foundation primitives, CLI automation,
library/packaging, AI session memory and
Obsidian vault integrations — from one-line artist generators
(create_feedback_network, create_audio_reactive, create_particle_system,
create_generative_art, …) to building blocks (create_control_panel,
animate_parameter, create_external_io for OSC/MIDI/DMX/NDI, …) down to
atomic node CRUD and inspection. Many systems arrive already playable, with
a control panel you can tweak, preset, or map to a controller. See the full,
always-current
tools reference and the
recipe gallery.
Optional: Creative RAG
A local, opt-in creative repertoire of open-licensed artworks/artists/techniques
the AI can search for inspiration. Off by default. Repertoire, not policy — no
bridge, DMX or Python exec. Enable with TDMCP_RAG_ENABLED=1 plus a local
Ollama install, then tdmcp creative-rag {sync|index|search}.
Full guide: docs/CREATIVE_RAG.md.
Security
The bridge runs arbitrary Python inside your TD process and listens on port
9980 on all interfaces — treat it like an open door to that machine. Run it only
on a trusted network, and for untrusted networks turn on bridge auth
(TDMCP_BRIDGE_TOKEN) and/or disable the exec endpoints
(TDMCP_BRIDGE_ALLOW_EXEC=0). Details:
Security.
Contributing & development
Build with npm install && npm run build; run npm test, npm run typecheck,
npm run lint. Work on the docs with npm run docs:dev (the
tools reference is generated by
scripts/gen-tool-docs.ts). See CONTRIBUTING.md,
CHANGELOG.md, and the roadmap.
License
MIT — see LICENSE.
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