Skip to main content
Glama
Garmin MCP Server

Garmin MCP Server

by nitinmms

get_hrv_range

Read-only

Retrieve daily HRV summaries for a selected date range to analyze heart-rate variability trends over time.

Instructions

Get daily HRV summaries for a date range. Best for HRV trends.

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": {
      "start_date": "<start_date>"
    },
    "name": "get_hrv_range"
  }
}

Input Schema

NameRequiredDescriptionDefault
end_dateNo
start_dateYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description contributes one useful behavioral fact beyond that: the returned data is aggregated at daily granularity, which affects how results should be interpreted. It says nothing about pagination, maximum range size, or timezone handling, so the added context is thin.

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?

Two short sentences with the core scope (daily HRV, date range) front-loaded and no wasted prose. The second sentence is a vague value claim that could have been spent on date format or the end_date default instead.

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 no output schema, 0% schema coverage, and no parameter documentation anywhere, the description should at minimum pin down the date format and the semantics of an omitted end_date. For a date-range query tool where the whole contract is temporal, those omissions leave the agent guessing before the first call.

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 0% and neither parameter is documented in the schema, so the description carries the full burden. 'For a date range' implies that start_date and end_date pair together, but there is no date format, no timezone, and no explanation of what happens when the optional end_date is omitted (it defaults to null, which is unexplained). This is a meaningful gap for a tool whose entire behavior is date-driven.

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 states a specific verb and resource ('Get daily HRV summaries') plus a scope ('for a date range'), so an agent can tell it is a range-based HRV reader rather than a point read. It does not name the closely related sibling 'get_hrv', so the boundary between the single-value and range variants is left to inference.

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?

'Best for HRV trends' implies a usage context (aggregate trend analysis over many days), which is a soft routing hint. There are no explicit when-not-to-use conditions and no named alternative such as get_hrv for a single-day reading, so the agent must infer the split from the names alone.

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