apple-health-mcp-server: MCP server for querying Apple Health data with natural language using DuckDB under the hood

by the-momentum · MCP Server · ★ 249

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

appleapple-healthapplehealthhealthhealthcarehealthcare-applicationhealthtechmcp-serverwearable

Quick Facts

Stars249
Forks22
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score62/100
Open Issues7
Last Updated2026-07-09
Created2025-07-02
Platformsmcp, python
Est. Tokens~11k

About apple-health-mcp-server

Apple Health MCP Server Apple Health Data Exploration [!NOTE] This project has evolved into Open Wearables - self-hosted platform to unify wearable health data from multiple devices, including Apple Health. Open Wearables also provides an MCP server and a companion app for continuous Apple Health data sync, eliminating the need for manual XML exports.

Compatible Skills

These tools work well together with apple-health-mcp-server for enhanced workflows:

  • HiMe — semantic(0.29)+complementary+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • apple-health-analyst — semantic(0.55)+complementary+rare_topics+similar_pop (59%)
  • HealthFlow — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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