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garmin-connect-api

Get HRV

get_hrv
Read-onlyIdempotent

Fetch a night's heart rate variability in milliseconds, including nightly average, five-minute high, weekly average, Garmin status, and baseline band for a given date.

Instructions

Return one night's heart rate variability in milliseconds: the night's average and five-minute high, the weekly average, Garmin's status (such as BALANCED, UNBALANCED or LOW) and the baseline band.

A night belongs to the day it ended on: last night is today's date. With no HRV for that night the reply is null, or its fields are. The nightly average and status are also in get_day_summary; over weeks or months use get_trend with metric hrv. include_readings adds the five-minute readings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayYesThe date the night ended on; last night is today.
include_readingsNoAdd the five-minute readings.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds real behavioral context beyond that: null handling ('the reply is null, or its fields are') and the date-membership rule ('a night belongs to the day it ended on'). It stops short of auth/rate-limit detail, so a 4 rather than 5.

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?

Front-loads the return fields, then the date and null semantics, then the sibling pointers. Efficient overall, though the include_readings sentence largely duplicates the schema field description.

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?

With an output schema present, return values need not be spelled out, and the description still covers fields, date semantics, null behavior, and alternatives. Nothing an agent needs to invoke the tool correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters are already documented in the schema. The description reinforces the 'day' semantics and 'include_readings adds the five-minute readings' but adds little beyond what the schema already states, matching the baseline 3 for full-coverage schemas.

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?

States a specific verb and resource ('Return one night's heart rate variability in milliseconds') and enumerates the exact fields returned (night average, five-minute high, weekly average, status, baseline band). It also distinguishes itself from siblings by explicitly naming get_day_summary and get_trend, so an agent can route without opening other schemas.

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?

Names the alternatives and the conditions that select them: nightly average/status are also in get_day_summary, and 'over weeks or months use get_trend with metric hrv'. This is explicit when-to-use/when-to-use-elsewhere guidance.

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