garmin-mcp-triathlon

update_custom_food

Modify an existing food in your Garmin nutrition library while keeping omitted nutrients unchanged. Fetch the foodId and servingId first, then pass only the fields you want to edit.

Instructions

Update an existing custom food in the user's Garmin nutrition library

Fetches the food's current record before writing so that omitted optional fields (brand, carbs, protein, fat, micros, etc.) preserve their existing values rather than being cleared. Only the fields you explicitly pass are changed; everything else is carried forward from the current record.

All nutrient amounts are ABSOLUTE values per serving, not %DV. Nutrition labels often print %DV for calcium/iron/vitamin D — convert to absolute units before passing.

Use get_custom_foods first to find the foodId and servingId.

Args: food_id: ID of the custom food to update (from get_custom_foods) serving_id: Serving ID of the food (from get_custom_foods) food_name: Name of the custom food calories: Calories per serving serving_unit: Unit for serving size (e.g. "G", "ML", "OZ"). Default "G" number_of_units: Serving size in the specified unit. Default 100 brand_name: Brand or vendor name; omit to preserve the existing value carbs: Carbohydrates in grams per serving protein: Protein in grams per serving fat: Total fat in grams per serving fiber: Fiber in grams per serving sugar: Sugar in grams per serving saturated_fat: Saturated fat in grams per serving sodium: Sodium in mg per serving cholesterol: Cholesterol in mg per serving potassium: Potassium in mg per serving trans_fat: Trans fat in grams per serving calcium: Calcium in mg per serving (NOT %DV) iron: Iron in mg per serving (NOT %DV) vitamin_d: Vitamin D in mcg per serving (NOT %DV)

Input Schema

TableJSON Schema

Name	Required	Default
`fat`	No
`iron`	No
`carbs`	No
`fiber`	No
`sugar`	No
`sodium`	No
`calcium`	No
`food_id`	Yes
`protein`	No
`calories`	Yes
`food_name`	Yes
`potassium`	No
`trans_fat`	No
`vitamin_d`	No
`brand_name`	No
`serving_id`	Yes
`cholesterol`	No
`serving_unit`	No	G
`saturated_fat`	No
`number_of_units`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 4, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the fetch-before-write behavior, that omitted optional fields preserve existing values rather than being cleared, and that nutrients are absolute values (not %DV). It omits error/auth behavior and permission requirements, keeping it short of a 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?

Well front-loaded: purpose, then the critical preserve-omitted-fields semantics, then the %DV warning, then the arg list. Slightly redundant (the preserve-existing-values point is restated twice) but every block is relevant and earn its place.

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?

For a 20-parameter mutation tool with no annotations, the description covers behavior and parameter semantics thoroughly, and an output schema exists so return values need not be explained. Missing only edge-case/error handling, which keeps it from a 5.

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 the description must compensate — and it does, annotating all 20 parameters with meaning and units (grams, mg, mcg) and explicitly flagging that calcium/iron/vitamin_d are NOT %DV. This adds substantial value the bare schema lacks.

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+resource: 'Update an existing custom food in the user's Garmin nutrition library.' This clearly distinguishes it from create_custom_food, delete_custom_food, and get_custom_foods without needing to open any schema.

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

Usage Guidelines4/5

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

Explicitly instructs 'Use get_custom_foods first to find the foodId and servingId,' establishing a clear prerequisite workflow. It gives strong usage context but does not name alternate tools (create_custom_food) or state when-not-to-use.

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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