# mcp-peloton

> mcp-peloton — markswendsen-code-mcp-peloton. Use this tool when you need to manage Peloton workouts programmatically, allowing AI agents to control and optimize exercise routines. It solves problems related to automated workout scheduling and customization, providing a seamless interface for AI-driven fitness management. With git integration, it accepts workout data as input and outputs optimized training plans.

Canonical page: https://skillsregistry.net/skills/markswendsen-code-mcp-peloton  
JSON: https://api.skillsregistry.net/v1/skills/markswendsen-code-mcp-peloton

## Description

MCP server for Peloton - let AI agents manage workouts

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/markswendsen-code/mcp-peloton)

## 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": "markswendsen-code-mcp-peloton"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/markswendsen-code-mcp-peloton` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/markswendsen-code-mcp-peloton/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