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Glama
JJRPF

Garmin MCP Server

by JJRPF

add_body_composition

Log weight and body composition metrics like body fat, muscle mass, hydration, and metabolic rates to Garmin Connect for comprehensive health tracking.

Instructions

Add body composition data

Args: date: Date in YYYY-MM-DD format weight: Weight in kg percent_fat: Body fat percentage percent_hydration: Hydration percentage visceral_fat_mass: Visceral fat mass bone_mass: Bone mass muscle_mass: Muscle mass basal_met: Basal metabolic rate active_met: Active metabolic rate physique_rating: Physique rating metabolic_age: Metabolic age visceral_fat_rating: Visceral fat rating bmi: Body Mass Index

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bmiNo
dateYes
weightYes
basal_metNo
bone_massNo
active_metNo
muscle_massNo
percent_fatNo
metabolic_ageNo
physique_ratingNo
percent_hydrationNo
visceral_fat_massNo
visceral_fat_ratingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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 only lists arguments and units; it does not explain whether adding data overwrites an existing date, whether entries are upserted, what happens with omitted optional fields, or what the response contains. This is a notable gap for a mutation tool.

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 a compact one-line summary followed by a clean argument list. Every line adds information, and there is no filler or redundancy, making it easy for an agent to scan and extract parameter semantics quickly.

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

Completeness4/5

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

Given the high parameter count and 0% schema description coverage, the description does a solid job of covering input semantics. An output schema exists, so return-value details are not required. The main missing context is behavioral: overwrite semantics, idempotency, and any prerequisite state are not addressed.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it does by listing all 13 parameters with human-readable meanings and units, such as date in YYYY-MM-DD and weight in kg. It adds useful context beyond the bare schema property names, though it stops short of providing ranges, constraints, or examples.

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 states a specific verb and resource: "Add body composition data". This clearly distinguishes it from read-style siblings like get_body_composition and from add_weigh_in, though it does not explicitly name those alternatives.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus related write tools such as add_weigh_in or add_weigh_in_with_timestamps. The intended use is implied by the name and argument list, but there are no explicit conditions, prerequisites, or exclusions.

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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