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VSidhArt

Intervals.icu MCP Server

by VSidhArt

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:

{
  "name": "get_grouped_wellness",
  "arguments": {
    "oldest_date": "2024-01-01",
    "newest_date": "2024-03-31",
    "group_by": "month",
    "include_details": false
  }
}

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

TableJSON Schema
NameRequiredDescriptionDefault
oldest_dateYes
newest_dateNo
group_byNomonth
include_detailsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it groups data over time periods, returns averages and statistics, and includes warnings about common mistakes (e.g., invalid group_by values). However, it lacks details on error handling beyond the 'Raises' section, such as rate limits or authentication needs, which could be more explicit for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (e.g., 'Best for:', 'Parameters', 'Returns'), but it is somewhat lengthy due to including examples and detailed parameter info. Every sentence adds value, such as usage examples and tool relationships, but it could be more front-loaded by emphasizing key points earlier to improve efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the tool (4 parameters, no annotations, schema coverage 0%), the description is highly complete. It covers purpose, usage guidelines, parameters, returns (with an output schema provided), and error handling. The presence of an output schema reduces the need to explain return values in detail, and the description supplements this adequately with examples and contextual info.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate fully. It does so by providing detailed parameter semantics in the 'Parameters' section, including data types, formats (e.g., YYYY-MM-DD), optionality, default values, and allowed options for 'group_by' (e.g., 'week', 'month', 'all'). This adds significant meaning beyond the basic input schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'fetching and grouping wellness data to show trends and patterns.' It uses specific verbs ('fetching,' 'grouping') and distinguishes from sibling tools by mentioning 'get_wellness' for individual records versus this tool for trend analysis. The title is null, so the description fully carries this burden.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance with 'Best for:' and 'Not recommended for:' sections, clearly stating when to use this tool (e.g., analyzing trends over time) versus alternatives (e.g., individual day analysis). It also includes 'Tool Relationships' that references sibling tools like 'get_wellness' for context on when to use this tool directly or after others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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