# io.github.rjn32s/mcp-yolo

> Use this tool when you need to detect and segment objects in images or videos without prior training, leveraging zero-shot learning capabilities. It solves problems in computer vision, such as object recognition and scene understanding, by providing accurate detections and segmentations. The tool takes in image or video inputs and outputs bounding boxes, class labels, and segmentation masks, making it ideal for applications like surveillance, robotics, and autonomous systems.

Canonical page: https://skillsregistry.net/skills/io-github-rjn32s-mcp-yolo  
JSON: https://api.skillsregistry.net/v1/skills/io-github-rjn32s-mcp-yolo

## Description

An MCP server providing zero-shot object detection and segmentation using Ultralytics YOLOE.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 0.1.2
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.rjn32s%2Fmcp-yolo)
- **Repository:** <https://github.com/rjn32s/mcp-yolo>

## 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": "io-github-rjn32s-mcp-yolo"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-rjn32s-mcp-yolo` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-rjn32s-mcp-yolo/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