Critical gaps across all dimensions. The single tool 'predict_nightly_recovery' has a description but no visible input schema, parameter definitions, or output schema in the provided source. The tool is mentioned only in a Dockerfile comment and pyproject.toml dependency list, no explicit tool registration code is visible. Without seeing the actual tool implementation (tools/predict_recovery.py is referenced but not provided), the tool definition cannot be verified. The description is adequate (owner-only tool for predicting nightly recovery using frozen s01 champion model) but lacks parameter details, return type documentation, and error handling guidance. No evidence of input validation, pagination, or structured output design.

• Provide the complete tool implementation file (tools/predict_recovery.py) showing explicit tool registration with FastMCP, including input schema with typed parameters and descriptions.
• Document the output schema: what fields does predict_nightly_recovery return? (e.g., recovery_score, confidence, timestamp, recommendations). Include field types and descriptions.
• Add parameter descriptions: clarify what 'privacy consent' means (boolean flag? signed agreement?), what 'valid wearable data' requires (device type, data freshness, minimum samples?), and what identifiers the tool accepts (user_id, device_id, date range).
• Implement error handling with recovery guidance: 'Privacy consent not found. Call get_user_consent() first.' or 'Wearable data missing for 2024-01-15. Available dates: 2024-01-14, 2024-01-13.'
• Consider renaming to 'predict_recovery_score' or 'get_nightly_recovery_prediction' to clarify the action and return type, and add an enum constraint for any categorical inputs (e.g., recovery_level: [low, medium, high]).