# ai.dragoneye/mcp

> Use this tool when you need to build zero-shot video models for object detection and classification tasks, solving problems such as automated video analysis and content understanding. It takes in video data as input and outputs classified objects, categories, and attributes. Ideal for applications requiring real-time video analysis, such as surveillance, robotics, and autonomous systems.

Canonical page: https://skillsregistry.net/skills/ai-dragoneye-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ai-dragoneye-mcp

## Description

Build zero-shot video models for object detection, and category and attribute classification.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-05-14

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/ai.dragoneye%2Fmcp)

## Use it

MCP endpoint published by the skill: `https://nexus.api.dragoneye.ai/mcp`

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": "ai-dragoneye-mcp"
    }
  }
}
```

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