garmin-mcp

get_body_composition

Retrieve daily weight, body fat, and muscle-mass trends from Garmin Connect for a custom date range, including latest weight and period averages.

Instructions

Weight, body fat, and muscle-mass trend over recent days.

Returns one row per day Garmin has a reading for, plus the latest weight and average over the window.

Args: end_date: End of the window in YYYY-MM-DD format. Defaults to today. days: Window size in days. Capped at 90.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_body_composition"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No
`end_date`	No

Output Schema

Name	Required	Description	Default
`days`	No
`note`	No
`avg_weight_kg`	No
`latest_weight_kg`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 24, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the return format (one row per day, latest weight, average) and capping at 90 days, but omits other behavioral traits like authentication or idempotency.

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?

Reasonably concise, covering purpose and args in a few sentences. Slightly verbose but no wasted words. Front-loaded with main purpose.

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 tool's simplicity (2 optional params) and presence of output schema, the description adequately explains inputs and output shape. Lacks mention of potential errors or advanced usage.

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

Parameters5/5

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

Schema description coverage is 0%, so description must compensate. It does so by explaining both parameters: end_date format with default, days default and cap, adding clarity beyond the bare schema.

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 states it returns weight, body fat, and muscle-mass trend over recent days. It differentiates from siblings like get_sleep or get_body_battery by focusing on body composition, though it doesn't explicitly contrast with similar tools.

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

Usage Guidelines3/5

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

Usage is implied: use when you need body composition data. No explicit guidance on when not to use or alternatives among siblings, leaving the agent to infer.

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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