TD_MCP
A local-first MCP server for TouchDesigner that provides offline documentation retrieval and live control of TouchDesigner sessions, enabling AI-assisted network building and parameter management.
README
TouchDesigner MCP (TD_MCP)
Local-first TouchDesigner MCP (Model Context Protocol) that fuses the best ideas from the TD-MCP ecosystem into one tool:
- Offline doc/RAG server (
td_mcp.server_offline) — parallel multi-RAG: several retrieval backends (global BM25, per-source BM25 for operators/python/glsl/tutorials, optional MiniLM dense + HyDE, title boost) run concurrently and fuse via Reciprocal Rank Fusion, with an optional CrossEncoder reranker. Version-aware, no cloud/keys. - Eval harness (
td_mcp.rag.eval) — recall@k / MRR / nDCG over labelled queries, so quality is provable and regressions are caught. Includes 1091 chunks (TOP 49 / CHOP 35 / SOP 32 / DAT 24 / Python 22 / COMP 21 / tutorial 19 / POP 8 / GLSL 6 / etc. seed + tdmcp/bottobot corpora): k=5 recall 0.966, zero version-gating violations. - Live Bridge MCP Server & CLI Client (
td_mcp.server_live) + TD-side Bridge (bridge/td_mcp_bridge.py) — control a running TD session over HTTP using stdio MCP, Streamable HTTP + SSE (multi-session, DNS-rebind guard), or the legacy CLI. Exposes 39 live tools (create/delete/wire/inspect/read/verify/snapshot/recipe/timeline…). Every mutation is wrapped inui.undoso one Ctrl+Z reverts a whole agent batch. Features spatial context markers (*hereand*this) to resolve the currently active network or selected node. - Natively Embedded Chat UI Panel (
bridge/chat_ui.html) — a zero-dependency glassmorphic chat panel served by the bridge atGET /, with provider selector (Ollama / Gemini / OpenAI), live network sidebar, and a multi-step autonomous agent loop. - Autonomous in-TouchDesigner Agent (
bridge/td_mcp_agent.py) — a zero-dependency OpenAI-compatible agent script that runs natively inside TD Text DATs. Allows you to chat offline (using local Ollama) or online (Gemini/OpenAI) to build networks autonomously.
Design notes and the full server catalog are located in the repository root:
- [TouchDesigner_MCP_Servers.md](file:///c:/Users/Z/Downloads/PROJECTS/TOUCHDESIGNER/td-mcp/TouchDesigner_MCP_Servers.md) — catalog of every TD MCP server + brainstorm
- [TD_MCP_Master_Plan.md](file:///c:/Users/Z/Downloads/PROJECTS/TOUCHDESIGNER/td-mcp/TD_MCP_Master_Plan.md) — the master plan and future roadmap this scaffold implements
- [TouchDesigner_Links.md](file:///c:/Users/Z/Downloads/PROJECTS/TOUCHDESIGNER/td-mcp/TouchDesigner_Links.md) — official docs / Python API / curriculum
Layout
td-mcp/
├── pyproject.toml
├── HOW_TO_USE.md
├── README.md
├── setup_env.ps1
├── td_mcp/
│ ├── server_offline.py # doc/RAG MCP server + CLI (24 tools, ParallelRetriever)
│ ├── server_live.py # Streamable HTTP + SSE + stdio client for the TD bridge (39 tools)
│ ├── streamable_http.py # Streamable-HTTP transport mixin (SSE, sessions, DNS-rebind guard)
│ ├── generators.py # artist network generators (feedback / audio-reactive / particle / 3D / GLSL / LED / DMX)
│ ├── recipe_vault.py # recipe blueprint storage
│ ├── eval.py # offline build eval gate
│ ├── rag/
│ │ ├── retriever.py # Index (BM25 + dense + version resolver)
│ │ ├── strategies.py # per-source + dense + HyDE strategies, RRF fusion
│ │ ├── rerank.py # optional CrossEncoder reranker
│ │ ├── knowledge_graph.py # Graph-RAG related-operator walk
│ │ └── eval.py # recall@k / MRR / nDCG harness
│ ├── tdn/ # Diffable YAML (TDN) importer/exporter
│ ├── showcontrol/ # Show-control network builders (Art-Net / sACN / OSC / MIDI / timecode)
│ ├── led_mapping/ # LED pixel layout matrices and DMX mapping
│ ├── tools/risk.py # risk-tier classification (READ_ONLY / WRITE_ADDITIVE / DESTRUCTIVE)
│ └── kb/
│ ├── chunks.jsonl # 1091 built chunks (real TD facts, source+min_version+aliases)
│ ├── corpus.py # operator / Python / GLSL / version corpus records
│ ├── build_kb.py # regenerates chunks.jsonl from authored records
│ ├── import_corpus.py # import authored JSON corpus records
│ ├── embeddings.jsonl # MiniLM vectors (after TD_MCP_DENSE=1)
│ ├── scrape.py # crawl docs.derivative.ca
│ └── build_index.py # validate / dense-embed
├── bridge/
│ ├── td_mcp_bridge.py # paste into a Text DAT in TD (bridge server: JSON-RPC, WS, SSE, chat UI)
│ ├── td_mcp_agent.py # paste into a Text DAT in TD (autonomous builder agent, ~48 schemas)
│ ├── chat_ui.html # glassmorphic chat UI panel served at GET /
│ └── bootstrap.py # one-click bootstrap helper
└── skills/td-building/ # Claude Code skill
Quick start (no deps)
uv is enough — the retriever is pure standard library.
uv run python td_mcp/rag/retriever.py "blur top parameters"
uv run python -m td_mcp.server_offline "movie file in top param file"
uv run python -m td_mcp.server_offline --family TOP # list all TOPs
uv run python -m td_mcp.server_offline --parameter "movie" # param spec by nickname
uv run python -m td_mcp.server_offline --glossary # full KB index
Run as MCP Servers
Register in your AI client (Claude Desktop: %APPDATA%\Claude\claude_desktop_config.json; Cursor: %USERPROFILE%\.cursor\mcp.json):
{
"mcpServers": {
"td-mcp-offline": {
"command": "uv",
"args": ["run", "--project", "C:/Users/Z/Downloads/PROJECTS/TOUCHDESIGNER/td-mcp", "td-mcp-offline", "--mcp"]
},
"td-mcp-live": {
"command": "uv",
"args": ["run", "--project", "C:/Users/Z/Downloads/PROJECTS/TOUCHDESIGNER/td-mcp", "td-mcp-live", "--mcp"],
"env": {
"TD_MCP_AUTH_TOKEN": "YOUR_AUTO_GENERATED_TOKEN"
}
}
}
}
Exposed Offline Tools (24): td_docs_search, td_docs_operator, td_docs_python, td_docs_glsl, td_docs_template, td_docs_version, td_docs_family, td_docs_parameter, td_docs_compare, td_docs_connections, td_docs_workflow, td_docs_version_info, td_docs_related, td_docs_glossary, td_build_network, td_showcontrol_plan, td_led_map, td_build_feedback, td_build_audio_reactive, td_build_particle, td_build_3d_scene, td_build_glsl_shader, td_build_led_wall, td_build_dmx_fixture.
Exposed Live Tools (39): create_node, delete_node, set_parameters, get_parameters, get_errors, execute_python, list_nodes, project_info, capture_viewport, get_resource, describe_td_tools, batch, read_chop, read_top, read_dat, scan_network, build_and_verify, connect_nodes, rename_node, copy_node, auto_layout, get_node, set_node_color, set_node_comment, map_network, disconnect_nodes, get_connections, exec_node_method, snapshot_network, restore_network, get_performance, validate_network, set_flags, find_nodes, set_node_position, timeline, export_recipe, import_recipe, save_tox.
Control a live TouchDesigner
- In TD, create a Text DAT, paste
bridge/td_mcp_bridge.py, and run:op('text1').module.start()— it listens on127.0.0.1:9980and prints the authorization token. - From the shell (CLI mode):
$env:TD_MCP_AUTH_TOKEN="YOUR_AUTO_GENERATED_TOKEN"
uv run td-mcp-live status
uv run td-mcp-live create /project1 CircleTOP --name my_circle
uv run td-mcp-live set /project1/my_circle '{"radius": 0.5}'
uv run td-mcp-live exec "print([c.name for c in op('/project1').children])"
Streamable HTTP mode
td-mcp-live can also run as a Streamable-HTTP MCP server (POST / JSON-RPC +
GET / SSE, multi-session via Mcp-Session-Id, DNS-rebind guard). Point an
HTTP-capable MCP client at http://127.0.0.1:8765:
uv run td-mcp-live --http
Grow the knowledge base
chunks.jsonl is generated by td_mcp/kb/build_kb.py from a curated set of
authored records (operators across every family, Python classes, GLSL, and
recipes). Edit the records there and regenerate — never hand-edit chunks.jsonl.
uv run python -m td_mcp.kb.build_kb # author -> chunks.jsonl (216 chunks)
uv run python -m td_mcp.rag.eval # re-check recall/MRR/nDCG
uv add --optional scrape
uv run python -m td_mcp.kb.scrape
uv run python -m td_mcp.kb.build_index
TD_MCP_DENSE=1 uv run python -m td_mcp.kb.build_index # all-MiniLM upgrade
Upgrade retrieval quality (opt-in, no forced download)
uv pip install -e ".[rag]" # sentence_transformers
TD_MCP_DENSE=1 uv run python -m td_mcp.kb.build_index # encode + write embeddings.jsonl
TD_MCP_DENSE=1 uv run td-mcp-offline "blur top parameters" # dense + HyDE strategies now active
TD_MCP_RERANK=1 uv run td-mcp-offline "..." # late-stage CrossEncoder rerank
The dense/HyDE/rerank paths are lazy: without .[rag] and TD_MCP_DENSE=1
the server still runs (TF-IDF + BM25 + title + per-source RRF fusion).
Evaluate (catch regressions)
uv run python -m td_mcp.rag.eval # zero-dep: k=5 recall 1.0
TD_MCP_DENSE=1 uv run python -m td_mcp.rag.eval --k 5
Fuse an EXTERNAL RAG server (multi-process, in parallel)
Beyond per-source local backends, the ParallelRetriever can fold in a
separate RAG process — e.g. cacheflowe/td-docs-mcp or
bottobot running as its own MCP server. Both run concurrently
and are merged by RRF into one answer. This is the "combine
multiple RAGs in parallel" design: two independent systems, one fused
result, the slow stdio round-trip hidden behind the lexical threads.
TD_MCP_REMOTE_MCP="uv run td-docs-mcp" uv run td-mcp-offline "blur top"
# optional: TD_MCP_REMOTE_TOOL / TD_MCP_REMOTE_ARG to match its tool name
The external hits appear in results with source: "remote:...".
Tests
uv run python -m tests.test_rag # recall/version/per-source + local+remote fusion
uv run python -m tests.test_mcp_server # server wiring: 6 tools, search returns KB
tests/fake_remote_mcp.py is a tiny stdio MCP server the
fusion test uses to exercise the multi-process path without a real
external install. (Note: the mcp 1.28 stdio transport has an
internal pydantic quirk on some builds, so the server test calls
handlers in-process rather than over the wire.)
Anchor live mutations to docs ("shots")
uv run td-mcp-live --anchor create /project1 "Blur TOP" TOP
Each OK response gets a shots field: the most relevant KB chunk(s),
so the LLM sees doc-truth next to what it just did in TD.
Port map (avoid clashes)
td-mcp bridge = 9980 (same as Pantani tdmcp & benoitliard
touch-mcp — run only one, or change TD_MCP_PORT). Others in the
ecosystem: Embody 9870, johnsabath 9988, twozero 40404,
superdwayne 8053.
License
MIT. Note: TrueFiasco/TD_Builder_alpha (a source of the hybrid-RAG
idea) is AGPL-3.0 — the techniques here are reimplemented, not
copied, so MIT stays clean.
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