garmin-connect-api

Add body composition

add_body_composition

Record a Garmin Connect weigh-in with body fat, water, muscle, bone, and BMI so a smart scale's full reading uploads in one entry.

Instructions

Record a weigh-in with body composition: fat, water, muscle, bone, BMI.

This is the way to bring a smart scale's full reading into Garmin. A plain add_weigh_in carries weight only. Garmin reports every upload as "processed"; an identical measurement sent again is still kept only once, but a different reading at the same moment is kept as a second weigh-in. To correct one, delete the wrong one first: list_weigh_ins gives its id.

Input Schema

TableJSON Schema

Name	Required	Description
`bmi`	No	Body mass index.
`timestamp`	Yes	When it was measured. Without an offset, it is taken as local time in the Garmin account's own timezone.
`weight_kg`	Yes	Body weight, kg.
`percent_fat`	No	Body fat, %.
`bone_mass_kg`	No	Bone mass, kg.
`metabolic_age`	No	In years.
`basal_met_kcal`	No	Basal metabolic rate, kcal a day.
`muscle_mass_kg`	No	Skeletal muscle mass, kg: Garmin shows this field under that name.
`active_met_kcal`	No	Active metabolic rate, kcal a day.
`physique_rating`	No	The scale's physique rating, 1 to 9.
`percent_hydration`	No	Body water, %.
`visceral_fat_rating`	No	The scale's visceral fat rating, 1 to 59.
`visceral_fat_mass_kg`	No	Visceral fat mass, kg.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 20, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the annotations by disclosing server-side dedup semantics: an identical measurement is kept once even though the write is not idempotent, while a differing reading at the same timestamp becomes a second weigh-in. It also explains that Garmin reports everything as 'processed' and that corrections require a delete-first flow — critical context for a non-idempotent mutation.

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?

Four tight sentences, front-loaded with the core purpose, then alternatives, then the dedup/correction rules. The dedup sentence is dense but every sentence earns its place; nothing is wasted.

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?

For a 13-parameter mutation with an output schema and annotation coverage for safety, the description supplies the missing operational nuance: alternative selection, dedup behavior, and the delete-to-correct workflow. Nothing an agent needs to invoke it correctly is absent.

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 every field is already documented in the schema, and the description's field list (fat, water, muscle, bone, BMI) merely summarizes a few of them. No format, unit, or constraint guidance is added beyond what the schema already provides, so the baseline of 3 applies.

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?

Opens with a specific verb+resource ('Record a weigh-in with body composition') and enumerates the fields captured. It explicitly distinguishes itself from the sibling add_weigh_in ('A plain add_weigh_in carries weight only'), so an agent can route between the two without opening either schema.

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?

States exactly when to pick this over the alternative (full smart-scale reading vs weight-only) and names the sibling. It also lays out the correction workflow: delete the wrong entry first, finding its id via list_weigh_ins.

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