get_grouped_wellness
Analyze wellness trends by grouping fitness data into time periods to identify patterns and track health metrics over time.
Instructions
Tool for fetching and grouping wellness data to show trends and patterns.
Best for: Analyzing wellness trends over time, creating health reports, tracking recovery patterns, monitoring training stress balance, identifying correlations. Not recommended for: Individual day analysis; real-time monitoring. Common mistakes: Using invalid group_by value; requesting details for very large datasets. Prompt Example: "Show me monthly wellness trends for 2024" or "Group my wellness data by week for the last 3 months" Usage Example:
Tool Relationships: Use after get_wellness to analyze patterns, or directly for trend analysis when individual records aren't needed. Returns: Grouped wellness data with averages and statistics per time period.
Parameters
oldest_date : str The oldest date to fetch wellness data from (format: YYYY-MM-DD). This parameter is required. newest_date : str, optional The newest date to fetch wellness data from (format: YYYY-MM-DD). If not provided, no upper date limit is applied. group_by : str, optional How to group wellness data. Options: "week", "month", "all". Default: "month" include_details : bool, optional Whether to include individual records in each group. Default: False
Returns
dict Dictionary containing: - status: "success" or "error" - groups: Dictionary of groups with summaries (when not "all") - summary: Aggregate statistics (when group_by is "all") - total_records: Total number of wellness records - Additional metadata based on grouping type
Raises
ValidationError: If parameters are invalid. IntervalsError: If the API request fails.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| oldest_date | Yes | ||
| newest_date | No | ||
| group_by | No | month | |
| include_details | No |