# freddy

> Use this tool when you need to integrate your wearable devices, rings, and training apps to access personalized health data insights. It solves the problem of fragmented health data by connecting multiple sources and providing a unified interface to query your AI about your own health metrics. With inputs from wearables and training apps, it outputs tailored health data analysis and insights through AI-driven queries.

Canonical page: https://skillsregistry.net/skills/coach-freddy-freddy  
JSON: https://api.skillsregistry.net/v1/skills/coach-freddy-freddy

## Description

Connect your wearables, rings and training apps, then ask your AI about your own health data.

## 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:** other
- **Updated:** 2026-06-13

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/coach.freddy%2Ffreddy)

## Use it

MCP endpoint published by the skill: `https://freddy.coach/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": "coach-freddy-freddy"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/coach-freddy-freddy` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/coach-freddy-freddy/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