# vred-mcp

> vred-mcp — krngrover6-autodesk-vred-mcp. Use this tool when you need to integrate AI functionality with Autodesk VRED Professional 2027, enabling inspection and control of 3D scenes. It solves problems of automated scene analysis and modification by providing a secure bridge for MCP-compatible AI clients to interact with VRED. The tool accepts scene data as input and outputs controlled scene modifications, such as selection, visibility, and transforms, making it ideal for use cases like automated design review and virtual product testing.

Canonical page: https://skillsregistry.net/skills/krngrover6-autodesk-vred-mcp  
JSON: https://api.skillsregistry.net/v1/skills/krngrover6-autodesk-vred-mcp

## Description

A local MCP server that enables MCP-compatible AI clients to inspect and control the currently open scene in a running Autodesk VRED Professional 2027 instance via a secure bridge, supporting read-only inspection and gated mutations like selection, visibility, transforms, and screenshots.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/kojzdy6r28)
- **Repository:** <https://github.com/krngrover6/autodesk-vred-mcp>

## 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": "krngrover6-autodesk-vred-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/krngrover6-autodesk-vred-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/krngrover6-autodesk-vred-mcp/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