Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Utilizes the TouchDesigner MCP servers (tdmcp and touchdesigner-mcp) to create TOP/CHOP chains, script operators, inspect parameters, and debug node networks.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill file instructs AI agents on how to construct real-time visual systems inside TouchDesigner using the tdmcp (MindDesigner) and touchdesigner-mcp (Backup) integrations. It details node building, operator classes, preview generation, and troubleshooting steps.
The TouchDesigner MCP (MindDesigner) server enables AI models to programmatically construct, connect, parameterize, and debug node networks in TouchDesigner. In the BDB OS architecture, this automates audio-reactive particle simulations, feedback systems, projection mapping, GLSL shader chains, and data visualizations.
tdmcp_bridge_package.tox or mcp_webserver_base.tox) is placed inside TouchDesigner. It listens on a local port and translates incoming JSON commands to Python operations (like parent().create(noiseTOP)).tdmcp (MindDesigner): Runs on port 9980. Focuses on creative visual engineering with 332 high-level generation tools.touchdesigner-mcp (Backup/Direct): Runs on port 9981. Focuses on atomic node CRUD and low-level scripting fallback.| Server | Use for | Fallback when |
|---|---|---|
bdb_td_minddesigner (tdmcp, port 9980) | Primary: high-level creative generation and structured inspection/edit tools. | Undocumented. |
bdb_td_backup (touchdesigner-mcp, port 9981) | Atomic node CRUD and low-level scripting. | bdb_td_minddesigner is unavailable (pair labelled primary/fallback; no other trigger documented). |
get_td_info to confirm the TouchDesigner bridge is reachable and running compatible versions.noiseTOP for a Noise TOP, constantCHOP for a Constant CHOP).find_td_nodes, get_td_node_parameters, update_td_node_parameters, get_td_node_errors) over raw Python execution. Use execute_python_script only when no structured tool fits the request.get_preview or get_inline_preview so the user can verify the rendering in real-time.tdmcp)create_audio_reactive(layer_name: string): Sets up a complete audio spectrum analyzer routing to video modifiers.create_feedback_network(parent: string, input_op: string): Creates a feedback loop using feedback, blur, level, and composite TOPs.create_particle_system(parent: string): Generates a GPU particle flow using SOPs, CHOPs, and TOPs.animate_parameter(op_path: string, par_name: string, chop_expr: string): Binds a node parameter to a CHOP expression.touchdesigner-mcp & fallback)create_td_node(type: string, path: string, name: string): Places a single node in a network container.delete_td_node(path: string): Deletes an operator.connect_nodes(from_op: string, to_op: string, from_connector?: int, to_connector?: int): Connects outputs to inputs.get_td_nodes(path: string): Lists all operators in a specific network.get_td_node_errors(path: string): Checks for active operator errors (red flags) recursively.update_td_node_parameters(path: string, parameters: object): Modifies parameter settings.Generate a feedback warp tunnel TOP chain:
// Step 1: Create noise source
// Tool: create_td_node(type="noiseTOP", path="/project1", name="noise_src")
// Step 2: Set noise parameters
// Tool: update_td_node_parameters(path="/project1/noise_src", parameters={"resolutionw": 1080, "resolutionh": 1080, "period": 2.5})
// Step 3: Set up feedback loop
// Tool: create_feedback_network(parent="/project1", input_op="/project1/noise_src")
Connect a series of CHOPs programmatically when structured connection tools are not flexible enough:
# Call tool: execute_python_script(script="...")
parent_comp = op('/project1')
lfo = parent_comp.create(lfoCHOP, 'oscillator')
math_op = parent_comp.create(mathCHOP, 'modifier')
null_op = parent_comp.create(nullCHOP, 'out_signal')
# Connect ports
math_op.inputConnectors[0].connect(lfo)
null_op.inputConnectors[0].connect(math_op)
# Set Math CHOP range multiplier
math_op.par.mult = 100.0
9980 (TDMCP_PORT environment variable)9981 (WebServer port)# Check if MindDesigner is reachable
curl http://localhost:9980/api/info
tdmcp_bridge_package.tox or mcp_webserver_base.tox..tox component directly into your active network pane (e.g. /project1).Alternative Textport Bootstrap Installation:
Open TouchDesigner Textport (Dialogs -> Textport and DATs), paste and run:
import urllib.request; exec(urllib.request.urlopen("https://github.com/Pantani/tdmcp/raw/v0.12.0/td/bootstrap.py").read().decode())
TouchDesignerClient ECONNREFUSED
.tox component into the project and that its internal server is toggled active.Operator compilation error or red flag
get_td_node_errors on the path of the flagged node to read the compiled compiler/interpreter traceback.Version Mismatch Warning
touchdesigner-mcp-td folder in your project, restart TouchDesigner, and drop in the new .tox.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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