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pluton74mac

garmin-mcp-triathlon

by pluton74mac

delete_custom_food

Permanently remove a custom food from your Garmin nutrition library by food ID. Use it to clean up unused entries; the food must not be referenced in a logged meal.

Instructions

Delete a custom food from the user's Garmin nutrition library

Permanently removes a custom food entry. The food must not be actively referenced in a logged meal to be deleted. Use get_custom_foods to find the foodId.

Args: food_id: ID of the custom food to delete — a 32-char hex string (from get_custom_foods or create_custom_food)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
food_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden and does disclose the key behavioral facts: the deletion is permanent, and there is a referential-integrity guard against foods used in logged meals. It does not say what happens on failure (error vs silent no-op) or whether the caller needs any special permission, which leaves a modest gap.

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?

Purpose and precondition are front-loaded, and the prose is tight. The trailing 'Args:' block is partly redundant with the schema but earns its place by supplying the hex-string format and provenance that the schema lacks.

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?

An output schema exists, so return values need no explanation, and for a single-parameter mutation the description covers the destructive nature and the blocking precondition. Remaining omissions are error semantics and auth expectations, which are secondary for an agent deciding whether and how to call it.

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 coverage is 0% and the schema itself only offers a bare anyOf(integer|string) with no description, so the description must compensate and largely does: it defines food_id as a 32-char hex string and names the two tools that produce one. Note a minor tension — the schema also permits an integer, while the description implies a hex string is expected.

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 first sentence states a specific verb and resource ('Delete a custom food from the user's Garmin nutrition library'), scoping it to the custom-food library rather than food logs or workouts. This is enough to distinguish it from delete_food_log, delete_workout, and update_custom_food without opening 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?

It gives a clear workflow pointer ('Use get_custom_foods to find the foodId') and an explicit precondition ('must not be actively referenced in a logged meal'), which functions as a when-not rule. It stops short of naming a sibling alternative for removing logged entries (delete_food_log), so routing between the two delete tools still requires inference.

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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