apple-health-semantic-mcp
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Alternatives to apple-health-semantic-mcp
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Related Servers
- AlicenseNot gradedqualityCmaintenanceLoads Apple Health export data into a local SQLite database and exposes tools to query health metrics and workout records via natural language.3MIT
- AlicenseAqualityBmaintenanceOpen-source MCP server for MetricBridge (formerly Health Export AI). 16 tools and 22 prompts let any MCP agent query 190 Apple Health metrics from files on your own machine. The server is read-only, has zero dependencies and makes zero network calls.14144 npm6MIT
- FlicenseBqualityDmaintenanceEnables Claude to answer natural language questions about your Apple Health data by analyzing local CSV exports, supporting metrics like steps, heart rate, sleep, and more.62-
- AlicenseBqualityCmaintenanceEnables users to query Apple Health metrics, workouts, and trends from CSV files exported via the Health Auto Export app. It allows MCP clients to analyze health data such as heart rate, sleep stages, and activity levels directly from local iCloud Drive storage.311 npm1MIT
- AlicenseAqualityCmaintenanceQuery your Apple Health data including sleep, heart rate, steps, workouts, and more by ingesting an exported XML archive into a local SQLite database.5MIT
- FlicenseNot gradedqualityBmaintenanceMCP server that enables LLMs to query Apple Health data such as steps, heart rate, sleep, and workouts via natural language, with secure cloud access through OAuth.-
TDQS
Scored across 3 tools
Each tool has a distinct role: health_schema provides metadata, health_query executes arbitrary SQL, and health_report generates a curated summary. The cross-references between them clarify when to use which, leaving no ambiguity.
All tools follow the consistent health_<noun> pattern: health_schema, health_query, health_report. This uniform convention makes the toolset predictable and easy to navigate.
With only three tools, the set is tightly scoped for a semantic layer: schema discovery, query execution, and report generation. Each tool is essential and the count is appropriate for the purpose.
The domain is read-only exploration of Apple Health data, and the three tools cover the full workflow: understanding the schema, running specific queries, and generating high-level summaries. There are no obvious gaps or dead ends.