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Glama

get_hr_zones

Retrieve heart rate zones per sport and their calculation basis (max HR, heart rate reserve, or lactate threshold) to correctly interpret zone data, as the same percentage band maps to different BPM.

Instructions

Get the heart rate zones configured on the account, per sport, and crucially which basis they are calculated from (max HR, heart rate reserve, or lactate threshold). The same percentage band maps to very different bpm depending on the basis, so check this before interpreting any zone the watch reports.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

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 disclosure burden and does well: it reveals that zones are per-sport and that the calculation basis (max HR, HRR, lactate threshold) materially changes the bpm mapping. It does not mention permissions or return shape, but for a no-param read tool the key behavioral nuance is surfaced.

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?

Two sentences, no filler. The core output (zones per sport) and the critical caveat (the calculation basis) are both front-loaded, with the interpretive warning following immediately after.

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?

No output schema exists, and the description conceptually covers what is returned (zones per sport plus their basis). Combined with zero input parameters, this is nearly complete; only minor details like whether the response is fixed-size or per-sport structured data are left implicit.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing to disambiguate in the schema, and the description introduces no parameters of its own.

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 and resource ('Get the heart rate zones configured on the account, per sport') and scopes it to account configuration, which distinguishes it from the sibling get_activity_hr_zones (zones tied to a specific activity). An agent can route between the two without opening either 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?

Gives clear usage context: 'check this before interpreting any zone the watch reports.' It does not explicitly name get_activity_hr_zones as the alternative for activity-scoped zones or state exclusions, so it falls short of the 5 bar.

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