# Blender Optics Simulator MCP Server

> Blender Optics Simulator MCP Server — emircbngl-blender-optics-simulator. Use this tool when you need to simulate and optimize optical systems, as it provides a physics-verified optical bench that exposes geometry and beam data as JSON, allowing AI agents to drive alignment and corrections. This tool solves problems related to optical system design, simulation, and optimization, enabling accurate and efficient analysis. It accepts JSON inputs and outputs ground-truth geometry and beam data, making it ideal for use cases that require precise optical simulations and automation.

Canonical page: https://skillsregistry.net/skills/emircbngl-blender-optics-simulator  
JSON: https://api.skillsregistry.net/v1/skills/emircbngl-blender-optics-simulator

## Description

An MCP server for Blender that exposes a physics-verified optical bench as JSON, enabling AI agents to read ground-truth geometry and beam data, and drive the bench through alignment and corrections.

## Trust

- **Trust score (0–1):** 0.52
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/gsqlerke9i)
- **Repository:** <https://github.com/emircbngl/blender-optics-simulator>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "emircbngl-blender-optics-simulator"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/emircbngl-blender-optics-simulator` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/emircbngl-blender-optics-simulator/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt