No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by 8beeeaaat · MCP Server · ★ 542
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is touchdesigner-mcp safe to install? View the security audit →
TouchDesigner MCP This is an implementation of an MCP (Model Context Protocol) server for TouchDesigner. Its goal is to enable AI agents to control and operate TouchDesigner projects. English / 日本語 Overview 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 Installation Please refer to the Installation Guide. If you are updating, please refer to the procedure in the Latest Release. MCP Server Features This server enables AI agents to perform operations in TouchDesigner using the Model Context Protocol (MCP). Tools Tools allow AI agents to perform actions in TouchDesigner. Desc
| Stars | 542 |
| Forks | 58 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.8647630450705/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-20 |
| Created | 2025-04-13 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
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touchdesigner-mcp is MCP server for TouchDesigner. It is categorized as a MCP Server with 542 GitHub stars.
touchdesigner-mcp is primarily written in TypeScript. It covers topics such as mcp, mcp-server, touchdesigner.
You can find installation instructions and usage details in the touchdesigner-mcp GitHub repository at github.com/8beeeaaat/touchdesigner-mcp. The project has 542 stars and 58 forks, indicating an active community.
touchdesigner-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to touchdesigner-mcp on Agent Skills Hub include Embody. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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