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 hhopke · MCP Server · ★ 92
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is intervals-icu-mcp safe to install? View the security audit →
Intervals.icu MCP Server A Model Context Protocol (MCP) server for Intervals.icu integration. Access your training data, wellness metrics, and performance analysis through Claude, ChatGPT, and other LLMs. Originally based on eddmann/intervals-icu-mcp (MIT licensed). This project is an independent continuation with significant bug fixes and new features — see CHANGELOG.md for details. Overview 58 tools spanning activities, activity analysis, activity messages, athlete profile, wellness, events/calendar, performance curves, workout library, gear, sport settings, and custom items — plus 4 MCP R
| Stars | 92 |
| Forks | 52 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 76.165976594025/100 |
| Open Issues | 11 |
| Last Updated | 2026-10-09 |
| Created | 2026-03-29 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~16k |
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intervals-icu-mcp is Read/write MCP server for Intervals.icu — training data, planning, and structured workout generation.. It is categorized as a MCP Server with 92 GitHub stars.
intervals-icu-mcp is primarily written in Python. It covers topics such as cycling, fitness, fitness-tracking.
You can find installation instructions and usage details in the intervals-icu-mcp GitHub repository at github.com/hhopke/intervals-icu-mcp. The project has 92 stars and 52 forks, indicating an active community.
intervals-icu-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to intervals-icu-mcp on Agent Skills Hub include trainingpeaks-mcp, garmin-connect-mcp, whoop-mcp. 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: