Tempo MCP
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- AlicenseAqualityCmaintenanceProvides Claude with real-time access to local health data including sleep, recovery, strain, and workouts from WHOOP and Apple Health, enabling informed context-aware interactions.61MIT
- AlicenseNot gradedqualityDmaintenanceConnects your Whoop health data to Claude, enabling natural language queries about recovery, sleep, strain, and workouts.81 npmMIT
- AlicenseNot gradedqualityDmaintenanceIntegrates WHOOP biometric data into Claude and other MCP-compatible applications, providing access to sleep analysis, recovery metrics, strain tracking, and biological age data through natural language queries.19 npm13MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude to securely read your own WHOOP account data — recovery, HRV, resting heart rate, sleep, strain, workouts and body profile — through the official WHOOP developer API on a self-hosted server. Supports natural-language questions such as whether you are ready to train hard today.19 npmMIT
- FlicenseNot gradedqualityDmaintenanceConnects iClips project data to Claude for agency capacity planning, exposing tools to monitor daily demand vs. capacity, stalled jobs, cascading off-work effects, and classification audits.-
- AlicenseNot gradedqualityDmaintenanceExposes Whoop fitness data (recovery, sleep, strain, workouts) to Claude for use as a daily training coach, enabling natural language queries about your health metrics and training readiness.MIT
TDQS
Scored across 11 tools
Each tool has a clearly distinct purpose: scoring, check-in logging, revenue/calendar ingestion, recommendations, action application, history, digest, license management, and export. No two tools overlap in function, and descriptions make boundaries explicit.
Most tools follow a consistent verb_noun pattern (log_checkin, ingest_revenue_event, recommend_actions, apply_action), but a few like tempo_score, weekly_digest, and license_status are noun_phrases, slightly breaking the pattern. Still, all names are snake_case and readable.
With 11 tools, the count is well within the ideal 3-15 range and each tool earns its place in the workflow—from data ingestion to scoring, recommendations, and administration. No redundancy or bloat.
The tool set covers the full domain lifecycle: input (check-in, revenue, calendar), analysis (tempo_score, recommend_actions), output (get_history, weekly_digest), execution (apply_action), and administrative functions (license_status, activate_license, export_data). Missing update/delete operations are not critical for this append-heavy logging use case.