Google Health Fitbit MCP
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- 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.26345 npm66MIT
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- FlicenseNot gradedqualityCmaintenanceSelf-hosted MCP server that provides read-only access to Google Health API v4, enabling analysis of personal health data via OpenAI Responses API or ChatGPT.-
- AlicenseAqualityDmaintenanceMCP server to read daily activity, sleep, heart rate, and body metrics from Google Health API, allowing AI assistants like Claude to access your health data. Optionally syncs health metrics to an Obsidian vault.5MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that connects AI clients to the Google Health API, enabling natural language queries about health and fitness data such as steps, sleep, heart rate, and exercise sessions.56 npm1MIT
- AlicenseBqualityAmaintenanceLocal-first MCP server that connects AI agents to your Fitbit activity, sleep, heart-rate, HRV, SpO2 and weight data.33135 npm4MIT
TDQS
Scored across 29 tools
Most tools have distinct purposes, but there is some overlap among summary/rollup tools (e.g., daily_rollup, rollup, daily_summary, weekly_summary, wellness_context) which may cause confusion for an agent deciding which to use. Overall, the set is well-segmented by function.
All tools follow a consistent pattern: prefix 'google_health_' followed by a verb_noun or adjective_noun (e.g., get_auth_url, list_data_types, daily_rollup). No mixing of conventions, making it predictable for agents.
With 29 tools, the count is above the typical 3-15 range, feeling slightly heavy. However, the scope covers many aspects of health data integration (auth, profiles, data points, summaries, devices, etc.), so it is borderline acceptable but could benefit from consolidation.
The tool set covers authentication, profile management, data retrieval (list, get, reconcile, rollups), summaries, device info, settings, and privacy. Missing are write operations for health data points (likely by design) and possibly more granular error handling, but the core workflows are well-covered.