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Glama

add_body_composition

Log body composition metrics like weight, fat percentage, muscle mass, and BMI into Garmin Connect for a specific date.

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.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior1/5

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

The description omits any details about side effects, such as whether the operation overwrites existing entries, requires prior data, or is idempotent. With no annotations and only a bare action phrase, the agent has no insight into the write behavior or potential consequences.

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?

The description is concise, consisting of a single action line followed by a parameter list. However, it is not structured as a narrative or front-loaded with a summary; it reads as a bare enumeration, which is efficient but could be more organized.

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 lacks information about the return value or success/failure indications, despite the presence of an output schema. It also does not highlight which parameters are required (date and weight) or any constraints, leaving the agent with incomplete context for invoking the tool correctly.

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?

The description adds moderate value by listing units for some parameters (e.g., weight in kg, percent_fat as percentage), but it does not provide units for all fields (e.g., basal_met) or clarify expected ranges. Given the schema has 0% coverage, this is helpful but incomplete, earning a middle score.

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 clearly states the verb 'Add' and the resource 'body composition data', making the tool's purpose unambiguous. It is also distinct from sibling tools like add_weigh_in and get_body_composition, so an agent can easily identify this as the write operation for body composition metrics.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives such as add_weigh_in or get_body_composition. There is no explanation of prerequisites, relationship to other data, or typical use cases, leaving the agent to infer context from the tool name alone.

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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