# Animal Map Vision MCP

> Animal Map Vision MCP — duongnad-mcp-vision. Use this tool when you need to visually inspect and validate 3D game maps for quality and accuracy. It solves problems related to geometry, navigation, ecology, and rendering issues, providing a comprehensive quality check. The Animal Map Vision MCP tool takes 3D game maps as input and outputs validated maps with identified issues, making it ideal for game development and testing contexts.

Canonical page: https://skillsregistry.net/skills/duongnad-mcp-vision  
JSON: https://api.skillsregistry.net/v1/skills/duongnad-mcp-vision

## Description

Enables AI agents to visually inspect and validate 3D game maps using Gemini vision and built-in quality checks for geometry, navigation, ecology, and rendering.

## 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:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/sgqi6m10nk)
- **Repository:** <https://github.com/DuongNAD/mcp-vision>

## 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": "duongnad-mcp-vision"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/duongnad-mcp-vision` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/duongnad-mcp-vision/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