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jonas-theobald

garmin-mcp-bridge

get_wellness

Retrieve daily wellness metrics such as HRV, resting HR, sleep, weight, and soreness to compare against an athlete's baseline and decide if a hard session is appropriate.

Instructions

Get daily wellness: HRV, resting HR, sleep, weight, CTL/ATL, soreness.

This is the autoregulation input. Check it before prescribing a hard session, and compare HRV and resting HR against the athlete's own recent baseline rather than population norms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
newestNo
oldestNo
days_backNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the burden of explaining behavior. It states the kind of data returned and frames it as baseline-relative, which is useful. However, it doesn't disclose return format, units, missing-data behavior, or date-range semantics, leaving some behavioral gaps.

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

Conciseness5/5

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

The description is compact and front-loaded: the first sentence states exactly what the tool returns, and the second provides actionable usage guidance. Every sentence earns its place with no filler or repetition.

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?

The description is helpful for understanding the tool's role, but it is incomplete for correct invocation: it fails to explain how the date-range parameters work, what the default behavior is, or what the response structure looks like. Since there is no output schema and no annotations, these gaps matter.

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

Parameters1/5

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

Schema description coverage is 0%, and the description makes no mention of the three parameters (newest, oldest, days_back). It doesn't explain how to specify date ranges or how days_back interacts with the other parameters, so the description provides no added meaning beyond the bare schema names and defaults.

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 opens with a specific verb and resource ('Get daily wellness') and enumerates the concrete metrics returned (HRV, resting HR, sleep, weight, CTL/ATL, soreness). This clearly differentiates it from sibling tools focused on activities, workouts, and athlete profile.

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 explicitly identifies this as the autoregulation input and directs the agent to check it before prescribing a hard session, and to compare HRV/resting HR against the athlete's own recent baseline. It gives clear context for when to use it, though it doesn't mention alternatives or explicit when-not-to-use conditions.

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