tdmcp — TouchDesigner MCP server
TouchDesigner MCP server, describe a visual to Claude, Cursor or Codex and it builds a real, playable node network (audio-reactive, generative, particle, 3D, feedback) with live knobs + MIDI/OSC/DMX, then checks for errors and previews its own work.
Is tdmcp — TouchDesigner MCP server safe? Thin or incomplete trust evidence. Review carefully before production use.
Compare tdmcp — TouchDesigner MCP server
How does it stack up against its MCP Servers neighbours?
Pick any agent to compare →In detail: tdmcp — TouchDesigner MCP server scores 53.9/100 (Grade C), ranked #824 of 1325 tracked open-source AI agent projects, on evidence coverage B (3 of 5 independent signal types). The public evidence: no package-provenance attestation found; OSSF Scorecard rates its supply-chain practices 4.5/10; 91% of recent commits are signed; last pushed 2026-08-15. Every point is earned from checkable signals — never paid placement. How scoring works →
How tdmcp — TouchDesigner MCP server compares in MCP Servers
- #462 SharePoint MCP Server 54.0 +0.1
- #463 Genieacs MCP 53.9 ±0
- #464 tdmcp — TouchDesigner MCP server 53.9 this agent
- #465 Google Health MCP 53.9 ±0
- #466 Plumb MCP 53.9 ±0
Bars show each HVTrust score; the tick marks tdmcp — TouchDesigner MCP server’s 53.9.
Where the 53.9 comes from
HVTrust dimensions vs the MCP Servers average
53.9 / 100 · 100.0% confidencetdmcp — TouchDesigner MCP server MCP Servers average (775 agents)
Quick Trust Read
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade C reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage B is separate — it grades how many independent signal types back the score (3 of 5), so a high score on thin evidence stays visible. Full methodology →
Rank Trend
Activity & Reach
Analysis
Activity Inputs
34.0 / 100Supply Chain Trust
Common questions about tdmcp — TouchDesigner MCP server
Does tdmcp — TouchDesigner MCP server publish package provenance?
Does tdmcp — TouchDesigner MCP server have an OpenSSF Scorecard?
Is tdmcp — TouchDesigner MCP server actively maintained?
What license does tdmcp — TouchDesigner MCP server use?
Are tdmcp — TouchDesigner MCP server's commits signed?
Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.
AI agent surface
MCP, providers, tool surface
These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →
- MCP signal live
- External deps live
- Tool / plugin surface live
- Package provenance drift live
Detected changes to tdmcp — TouchDesigner MCP server's runtime surface and supply-chain posture, from daily public-signal snapshots. A change here means our detectors see something different — a genuinely changed capability, or better evidence of an existing one.
Maintain tdmcp — TouchDesigner MCP server?
For maintainers
HVTrust scores tdmcp — TouchDesigner MCP server from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.
Reputation Timeline
Signal history
Embed Badge Badge guide for maintainers →
For maintainers
[](https://hvtracker.net/agents/tdmcp-touchdesigner-mcp-server)
<a href="https://hvtracker.net/agents/tdmcp-touchdesigner-mcp-server"><img src="https://hvtracker.net/badge/tdmcp-touchdesigner-mcp-server.svg" alt="HVTrust"></a>
Other agents in MCP Servers
GitHub REST API (repo, commits, stars, forks, license) · npm Registry (downloads, provenance) · OpenSSF Scorecard CLI
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.3 · Raw JSON