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JJRPF

Garmin MCP Server

by JJRPF

set_activity_feel

Log how an activity felt by setting Garmin's 5-point feel rating, from 0 (very tired) to 100 (strong), for any activity.

Instructions

Set how an activity felt ('How did you feel?').

Mirrors Garmin Connect's 5-point feel rating, stored as one of: 0 = very tired / poor 25 = tired 50 = normal 75 = good 100 = strong Higher is better.

Args: activity_id: ID of the activity to update feel: One of 0, 25, 50, 75, 100

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feelYes
activity_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the disclosure burden. It does clarify the mutation with 'Set' and adds useful behavioral detail by specifying the 5-point Garmin scale, exact stored values, and their meaning ('Higher is better'). It does not describe side effects or response behavior, though an output schema exists.

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

Conciseness5/5

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

The description is front-loaded with the purpose, uses a compact bullet-style list for the rating scale, and includes only necessary detail. The Args section repeats parameter names but adds essential semantics, so every part earns 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 two-parameter setter, the description covers accepted inputs, value semantics, and the target resource. Since an output schema exists, not explaining the return value is acceptable. The main gap is the lack of guidance on sourcing activity_id or any constraints on when the feel can be set.

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 description coverage is 0%, so the description must compensate. It documents both parameters in Args: activity_id as the activity to update, and feel with the complete list of allowed values and their labels. activity_id remains somewhat high-level, but feel semantics are fully covered beyond the schema.

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 uses a specific verb ('Set'), a clear resource ('how an activity felt'), and enumerates the exact rating values and labels. This distinguishes it from the other set_activity_* siblings, which target name, type, description, or event type.

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?

The intended use is implied by the purpose: use this when updating an activity's feel rating. However, the description does not explicitly state when to use this tool instead of alternatives, and it does not distinguish itself from the similar set_perceived_effort sibling.

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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