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VictorForV
by VictorForV

get_wellness

Retrieve an athlete's daily wellness and training-load history (CTL, ATL, ramp rate, sleep, HRV, mood) to assess whether training load is sustainable over the last 30 days.

Instructions

Daily wellness and training-load history.

    Defaults to the last 30 days. Per day: CTL (fitness), ATL (fatigue),
    ramp rate, plus whatever the athlete logs — weight, resting HR, HRV,
    sleep, soreness, fatigue, stress, mood, motivation. This is the record
    to read before judging whether load is sustainable.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
newestNo
oldestNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully states that it defaults to the last 30 days and describes the per-day fields. However, it does not disclose date-range application details, limits, output structure, or authentication/error behavior.

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 compact and front-loaded with the core purpose, followed by useful detail and a practical usage note. It is appropriately sized without redundant phrasing, though it could tighten the final sentence slightly.

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

Completeness2/5

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

For a tool with no annotations and no output schema, the description leaves important gaps: how to request a specific range, accepted date formats, maximum range, and the exact response shape. It gives a good overview but is not fully complete for an agent to invoke it correctly in all reasonable cases.

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

Parameters2/5

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

Schema description coverage is 0%, and the description never mentions the newest/oldest parameters. 'Defaults to the last 30 days' implies these optional parameters serve as range bounds, but the agent is left to infer their format, whether both must be supplied, and how they interact.

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

Purpose4/5

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

The description clearly identifies the resource as daily wellness and training-load history and enumerates the main metric groups (CTL, ATL, ramp rate, athlete logs). It conveys this is the record to read when evaluating load sustainability, which helps distinguish it from siblings like get_training_readiness, though it does not explicitly name any alternative.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: 'This is the record to read before judging whether load is sustainable.' It implies the historical, broad wellness scope without explicitly listing when not to use it or naming alternative tools such as get_training_readiness or get_training_load_chart.

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