amazfit-mcp
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- FlicenseNot gradedqualityCmaintenanceEnables MCP clients to access Zepp/Amazfit health and fitness data such as steps, sleep, heart rate, workouts, and user profile information via tools.-
- FlicenseNot gradedqualityBmaintenanceEnables read-only access to Amazfit/Zepp fitness data via the unofficial Zepp cloud API, exposing normalized health and workout metrics to MCP clients.-
- AlicenseAqualityAmaintenanceMCP server that reads Zepp/Amazfit health and workout data, exposing tools for daily summaries, sleep, heart rate, and workout details to any MCP client.83MIT
- AlicenseNot gradedqualityBmaintenanceExposes personal health data (recovery, sleep, strain, etc.) as MCP tools for AI agents to query and analyze.3MIT
- AlicenseNot gradedqualityCmaintenanceGives any MCP-compatible AI assistant secure, read-only access to your personal Strava fitness data, including activities, segments, routes, gear, and more through natural language.MIT
- AlicenseBqualityAmaintenanceEnables local, read-only access to Garmin Connect health and training data through MCP, letting assistants summarize recovery, sleep, activities, and more without uploading your data.19MIT
TDQS
Scored across 8 tools
Each tool targets a distinct resource or data view: connection status, devices, aggregated workout summaries, workout lists, individual workout detail, raw track data, daily activity/sleep, and health metrics. Even within the workout domain, list/detail/track/summarize have clear boundaries reinforced by the descriptions.
Most tools follow a consistent get_/list_/summarize_ snake_case pattern (e.g., get_devices, list_workouts, summarize_workouts). The only minor deviation is zepp_status, which uses a noun-style name rather than a verb-prefixed one.
Eight tools is a well-scoped set for a health/fitness data server. Each tool covers a distinct aspect of the domain without redundancy or bloat, and the count sits comfortably in the ideal range.
The tool surface covers the main Zepp/Amazfit data domains well: connection, devices, workouts (list/detail/track/aggregate), daily activity, sleep, and health metrics. Minor gaps exist, such as lack of body-composition data or continuous 24/7 heart-rate retrieval, but agents can still accomplish core health-data workflows.