No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by shashankswe2020-ux · MCP Server · ★ 152
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is whoop-mcp safe to install? View the security audit →
whoop-ai-mcp An MCP (Model Context Protocol) server that connects AI assistants like Claude to your WHOOP health and fitness data. Ask questions about your recovery, sleep, workouts, and more — all through natural conversation. 📦 Published on the MCP Registry as — discoverable by any MCP-compatible client. Features 🏋️ 6 health data tools — recovery, sleep, workouts, cycles, body measurements, and profile 🔐 Secure OAuth2 — browser-based authentication with automatic token refresh 🔄 Resilient — automatic retry on rate limits, token refresh on expiry, clear error messages 💾 Secure token storage — tokens stored at with permissions
| Stars | 152 |
| Forks | 54 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 80.5832709858896/100 |
| Open Issues | 32 |
| Last Updated | 2026-09-10 |
| Created | 2026-04-10 |
| Platforms | mcp, node |
| Est. Tokens | ~17k |
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whoop-mcp is MCP server to connect to whoop API. It is categorized as a MCP Server with 152 GitHub stars.
whoop-mcp is primarily written in TypeScript. It covers topics such as agentskills, fitness-tracker, mcp-server.
You can find installation instructions and usage details in the whoop-mcp GitHub repository at github.com/shashankswe2020-ux/whoop-mcp. The project has 152 stars and 54 forks, indicating an active community.
whoop-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to whoop-mcp on Agent Skills Hub include unbrowse, pm-skills, open-harness. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: