blender-ai-mcp-addon: AI assistant and MCP server for Blender: visual chat, scene tools, multi-agent workflows

by LiCinCan · MCP Server · ★ 976

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 65/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-assistantblenderblender-addonblender-aiblender-automationblender-extensionblender-mcpblender-pluginblender-pythonblender-scripting

Quick Facts

Stars976
Forks390
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score65/100
Open Issues31
Last Updated2026-10-21
Created2026-10-15
Platformsclaude-code, gemini, mcp, python
Est. Tokens~15k

About blender-ai-mcp-addon

mcp-for-blender-pro Your Blender scene. Your AI models. One creative workspace. An AI assistant for Blender that can inspect your scene, build objects, edit materials, find assets and carry out multi-step workflows—with a visual chat workspace and a built-in Model Context Protocol (MCP) server.

Compatible Skills

These tools work well together with blender-ai-mcp-addon for enhanced workflows:

  • mcp-for-blender-pro — semantic(0.89)+shared_fw(ollama,openai)+rare_topics+same_lang+similar_pop+shared_platform (91%)
  • blender-ai-mcp-addon — semantic(0.95)+shared_fw(ollama)+rare_topics+same_lang+similar_pop+shared_platform (83%)
  • blend-ai — semantic(0.69)+shared_fw(ollama)+rare_topics+same_lang+similar_pop+shared_platform (81%)
  • blenderwright — semantic(0.69)+shared_fw(ollama)+rare_topics+same_lang+similar_pop+shared_platform (81%)

blender-ai-mcp-addon alternative? Top 6 similar tools

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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