(So AI in touchdesigner is already a couple of years old, but this is a very updated method only a few weeks old)
TouchDesigner MCP acts as a bridge between AI models and the TouchDesigner WebServer DAT, enabling AI agents to:
Create, modify, and delete nodes
Query node properties and project structure
Programmatically control TouchDesigner via Python scripts
Heres a working example on the possibilites :
https://lobehub.com/mcp/8beeeaaat-touchdesigner-mcp
Project Structure Overview :
├── src/ # MCP server source code
│ ├── api/ # OpenAPI spec for TD WebServer
│ ├── core/ # Core utilities (logger, error handling)
│ ├── features/ # MCP feature implementations
│ │ ├── prompts/ # Prompt handlers
│ │ ├── resources/ # Resource handlers
│ │ └── tools/ # Tool handlers (e.g., tdTools.ts)
│ ├── gen/ # Code generated from OpenAPI schema for MCP server
│ ├── server/ # MCP server logic (connections, main server class)
│ ├── tdClient/ # TD connection API client
│ ├── index.ts # Main entry point for Node.js server
│ └── ...
├── td/ # TouchDesigner related files
│ ├── modules/ # Python modules for TouchDesigner
│ │ ├── mcp/ # Core logic for handling MCP requests in TD
│ │ │ ├── controllers/ # API request controllers (api_controller.py, generated_handlers.py)
│ │ │ └── services/ # Business logic (api_service.py)
│ │ ├── td_server/ # Python model code generated from OpenAPI schema
│ │ └── utils/ # Shared Python utilities
│ ├── templates/ # Mustache templates for Python code generation
│ ├── genHandlers.js # Node.js script for generating generated_handlers.py
│ ├── import_modules.py # Helper script to import API server modules into TD
│ └── mcp_webserver_base.tox # Main TouchDesigner component
├── tests/ # Test code
│ ├── integration/
│ └── unit/
├── .env # Local environment variables (git ignored)
├── dotenv # Template for .env
└── orval.config.ts # Orval config (TS client generation)
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