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garmin_save_custom_food

Create or update a custom food in Garmin Connect with calories, macros, sodium, serving size, and optional brand; use food_id to update an existing entry.

Instructions

Create or update a custom food (grams per serving, sodium in mg).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "calories": "<calories>"
    },
    "name": "garmin_save_custom_food"
  }
}

Input Schema

NameRequiredDescriptionDefault
fatNo
nameYes
brandNo
carbsNo
fiberNo
sugarNo
sodiumNo
food_idNoGive to update an existing custom food
proteinNo
caloriesYes
serving_idNo
serving_unitNoG, ML, SERVING, ... (garmin_reference)G
saturated_fatNo
number_of_unitsNoServing size in that unit

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

Annotations mark this as a non-read-only, non-destructive, closed-world write, but the description adds nothing about mutation semantics: whether omitted optional fields are cleared on update, whether the operation is reversible, what permissions or account state are required, or what is returned. For a 14-parameter write tool, that leaves the agent guessing about the most consequential behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence with no wasted words, and the core action is front-loaded. Brevity here tips into under-specification rather than economy, given the parameter surface it must cover.

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?

With 14 parameters, 21% schema coverage, and no output schema, the description is far too thin to make the tool callable with confidence. Nothing on units for the core macro fields or on update semantics is supplied anywhere the agent can see.

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

Parameters2/5

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

Schema description coverage is only 21%, so the description must compensate and it largely does not. It clarifies that sodium is in mg and that quantity is expressed per serving, but leaves the units for calories, fat, carbs, protein, sugar, and fiber, plus the meaning of serving_unit, number_of_units, brand, and serving_id, undocumented in both places.

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 uses a specific verb pair (create or update) on a specific resource (custom food), and the parenthetical signals unit conventions. It does not, however, distinguish this from siblings like garmin_log_food or garmin_food_search, so an agent must infer the boundary itself.

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?

There is no guidance on when to save versus log a food, or when to update an existing entry versus create a new one. The upsert rule ('give food_id to update') lives only in the schema's food_id description, not in the tool description.

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