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garmin-mcp

garmin-mcp

by Sinfjell
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_threshold_history

Retrieve lactate-threshold heart rate and pace history over a date range, aggregated daily, weekly, monthly, or yearly, to track changes across training blocks.

Instructions

Get lactate-threshold heart rate + pace over time (the threshold trend).

start_date and end_date are "YYYY-MM-DD" (start no more than a year before end). aggregation is one of "daily", "weekly" (default), "monthly", "yearly". Returns a JSON object with "points": a dated series, each entry giving lthr_bpm (threshold heart rate) and threshold pace (pace_per_km "M:SS", decimal pace_min_per_km, raw speed_m_s). Use this to see whether threshold HR/pace is trending up over a training block — for the single latest value use get_performance_metrics instead. If Garmin's range payload can't be parsed into a series it is returned verbatim under "raw".

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>",
      "end_date": "<end_date>"
    },
    "name": "get_threshold_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
end_dateYes
start_dateYes
aggregationNoweekly

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full burden and does well: it discloses the one-year maximum range constraint and the graceful-degradation behavior where an unparseable payload is returned verbatim under "raw". It does not cover data-availability prerequisites (e.g. whether a recent device sync is required) or rate limits, which keeps it short of a 5.

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?

The purpose and the sibling routing are front-loaded, and the parameter contract follows in a compact block. The sentence enumerating lthr_bpm, pace_per_km, pace_min_per_km and speed_m_s is largely redundant given an output schema exists, costing some tightness.

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 three-parameter read tool with an output schema, the description covers purpose, parameter syntax, the alternative tool, and the parse-failure fallback — enough to call it correctly. Missing only operational context such as data freshness or sync requirements.

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

Parameters5/5

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 entirely, and it does: it specifies the "YYYY-MM-DD" format for both dates, the start-before-end-within-a-year constraint, and the exact allowed values for aggregation along with its "weekly" default. Every parameter is fully specified in prose.

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 opening line names a specific verb and resource — lactate-threshold heart rate and pace over time — and frames it as a trend, which distinguishes it from point-in-time siblings. An agent can tell it apart from get_performance_metrics and get_running_threshold 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 an explicit when-to-use condition ("see whether threshold HR/pace is trending up over a training block") and routes the single-latest-value case to get_performance_metrics. The only gap is that get_running_threshold, arguably the most natural sibling for a single threshold reading, is never mentioned as the alternative.

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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