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 dhakalnirajan · MCP Server · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is blender-open-mcp safe to install? View the security audit →
blender-open-mcp Open Models MCP for Blender3D using Ollama Control Blender 3D with natural language prompts via local AI models. Built on the Model Context Protocol (MCP), connecting Claude, Cursor, or any MCP client to Blender through a local Ollama LLM. Architecture Three independent processes: FastMCP Server (): Exposes MCP tools over HTTP or stdio Blender Add-on (): TCP socket server running inside Blender Ollama: Local LLM serving natural language queries Installation Prerequisites Clone and set up bash git clone https://github.com/dhakalnirajan/blender-
| Stars | 113 |
| Forks | 31 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 76.018384438608/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-04 |
| Created | 2025-03-14 |
| Platforms | mcp, python |
| Est. Tokens | ~10k |
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blender-open-mcp is Open Models MCP for Blender Using Ollama. It is categorized as a MCP Server with 113 GitHub stars.
blender-open-mcp is primarily written in Python. It covers topics such as blender, blender-addon, blender3d.
You can find installation instructions and usage details in the blender-open-mcp GitHub repository at github.com/dhakalnirajan/blender-open-mcp. The project has 113 stars and 31 forks, indicating an active community.
blender-open-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to blender-open-mcp on Agent Skills Hub include blenderwright, blend-ai, roam-code. 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: