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Garmin MCP Server

Garmin MCP Server

by JJRPF
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_training_load_trend

Track daily training load metrics (CTL, ATL, TSB, ACWR) over a date range to evaluate whether an athlete is building fitness, peaking, or accumulating excessive fatigue.

Instructions

Get the Performance Management Chart (CTL/ATL/TSB) over a date range.

Returns Chronic Training Load (CTL, 42-day fitness), Acute Training Load (ATL, 7-day fatigue), Training Stress Balance (TSB = CTL - ATL, form/freshness), and Acute:Chronic Workload Ratio (ACWR) per day. Use this to assess whether the athlete is building fitness, peaking, or accumulating too much fatigue.

Recommended range: 4-8 weeks. Maximum: 90 days.

Args: start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format

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_training_load_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
end_dateYes
start_dateYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and handles it well by explaining each metric's time window (42-day fitness, 7-day fatigue), the TSB formula, and the ACWR. It adds date-range limits (recommended 4-8 weeks, max 90 days) and per-day output semantics. It does not cover potential error conditions or data-availability behavior, but those are minor for a read-only trend query and the output schema covers return structure.

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 well organized, front-loaded with the core purpose, followed by return details, usage guidance, and parameter documentation. Every sentence adds value and is skimmable, with formulas and ranges clearly highlighted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a two-parameter tool, a provided output schema, and no annotations, this description gives an agent everything needed to select and call the tool: purpose, metric definitions, use case, limits, and date format. The only minor omissions (error handling, inclusivity of end date) are unlikely to hinder correct invocation.

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%, and the description fully compensates by documenting both parameters with their required YYYY-MM-DD format. It also adds practical range constraints (recommended 4-8 weeks, maximum 90 days). This is exactly the semantic context the bare schema lacks.

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?

Description uses a specific verb 'Get' plus resource 'Performance Management Chart (CTL/ATL/TSB)' and enumerates the returned metrics (CTL, ATL, TSB, ACWR), clearly distinguishing it from sibling training-metric tools. It also defines the metrics in parenthetical terms, leaving no ambiguity about what the tool does.

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?

Explicitly states when to use the tool: 'Use this to assess whether the athlete is building fitness, peaking, or accumulating too much fatigue.' It also gives a recommended range (4-8 weeks) and a maximum (90 days). It does not name alternative sibling tools or explicitly state when not to use it, so it stops just short of a 5.

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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