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 dcc-mcp · MCP Server · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dcc-mcp-maya safe to install? View the security audit →
dcc-mcp-maya Bring Autodesk Maya to MCP-native AI agents. turns Maya into a standards-compliant MCP Streamable HTTP backend. Agents can inspect scenes, create geometry, edit materials, run animation and render workflows, export assets, and recover project state through typed tools instead of brittle ad-hoc scripts. The Maya plugin starts a Rust sidecar by default, so HTTP and gateway traffic stay isolated from Maya's UI thread while actual Maya API work is routed through Maya-safe dispatchers. []
| Stars | 58 |
| Forks | 8 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.4653146858562/100 |
| Open Issues | 9 |
| Last Updated | 2026-10-02 |
| Created | 2025-03-18 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
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dcc-mcp-maya is Autodesk Maya MCP adapter for AI agents to inspect scenes, model geometry, edit materials, rig and animate assets, render and export through typed tools in the DCC-MCP ecosystem.. It is categorized as a MCP Server with 58 GitHub stars.
dcc-mcp-maya is primarily written in Python. It covers topics such as 3d-modeling, ai-agents, animation.
You can find installation instructions and usage details in the dcc-mcp-maya GitHub repository at github.com/dcc-mcp/dcc-mcp-maya. The project has 58 stars and 8 forks, indicating an active community.
dcc-mcp-maya is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dcc-mcp-maya on Agent Skills Hub include kiln, blender-ai-mcp, three.ws. 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.
Sources & who's responsible: