health-os
Related Servers
Alternatives to health-os
No user-submitted related servers found.
Related Servers
- AlicenseCqualityDmaintenanceA local-first, model-agnostic MCP server that stores personal health data in a SQLite file and provides analysis-ready views for any AI client to log, retrieve, and reason over health records.79MIT
- AlicenseNot gradedqualityCmaintenanceEnables local-first personal health management with encrypted storage and scoped MCP tools for tracking vitals, medications, activities, emotions, sleep, encounters, and more.Apache 2.0
- AlicenseNot gradedqualityBmaintenanceA local-first MCP server for querying multi-omic personal health data (genome, labs, wearables) with an honesty contract and progressive disclosure skills.1AGPL 3.0
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to securely access, query, and mutate normalized user-controlled health data (e.g., from Apple Health or Supabase) through a bounded set of MCP tools, with optional OAuth and sandboxed deployment.MIT
- AlicenseDqualityBmaintenanceA local-first MCP server that provides safe, governed read-only SQL access to SQLite and PostgreSQL, with validation, limits, task tracking, and report generation.20MIT
- AlicenseBqualityAmaintenanceA local-first MCP server that enables AI agents to read user-authorized Google Health API v4 data from Fitbit, Pixel Watch, and partners via OAuth, with tokens never leaving the machine.26586 npm75MIT
TDQS
Scored across 36 tools
Most tools have clearly distinct clinical, nutritional, medication, lab, and habit scopes, and descriptions explicitly route between similar-looking tools such as query_food, query_nutrition, and nutrition_report. Some overlap remains among summary/report tools and the generic sql_query versus dedicated read tools, but an agent can usually select correctly.
The set overwhelmingly uses consistent snake_case names, mostly with recognizable verb_noun patterns such as get_medications, record_habit, update_meal, and approve_staged_source. Minor deviations include noun-first names like nutrition_report, sql_query, and crisis_resources, but the convention is still predictable.
With 36 tools, the set is large enough to impose significant selection overhead on an agent even for a broad health domain. Several read/report/summary tools could likely be consolidated or made more task-oriented without losing coverage.
Core workflows for meals, medications, habits, lab review, and reporting are well covered, but lifecycle gaps remain: allergies and diagnoses have create/read tools but no update/delete/correction tools. Visits, vaccinations, and hospitalizations appear in the timeline but have no obvious recording tools, which are notable omissions for a health OS.