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garmin-mcp

garmin-mcp

by marcelfortmann
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

get_training

Retrieve Garmin training metrics for a chosen date to check readiness, status, VO2 max, and fitness age. Use it to review daily training condition and recovery.

Instructions

Training metrics for a date. Pick via metrics: readiness, morning_readiness, status, vo2max, fitness_age.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_training"
  }
}

Input Schema

NameRequiredDescriptionDefault
dateNoDate in YYYY-MM-DD format. Default: today.
metricsNoWhich training metrics to return. Default: [readiness, status, vo2max].

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.0.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changedv1.1.1
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It says nothing about whether this is a read-only fetch, what the response shape is, how missing data for a date is handled, or whether readiness and morning_readiness differ — all material for a metrics tool.

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, front-loaded with the resource and followed by the selector. The only waste is the enumerated metric list, which duplicates the schema enum verbatim.

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

Completeness3/5

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

For a two-optional-parameter read tool with a fully documented schema, the description covers the basics. It still leaves open what each metric value contains and whether all requested metrics are guaranteed to return data for a given date.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both parameters already document format, defaults, and the enum. The description's metric list restates the schema enum rather than adding semantics (e.g., what 'morning_readiness' returns versus 'readiness'), so the baseline 3 applies.

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?

States a specific verb+resource ('Training metrics for a date') and enumerates the selectable metric families, so an agent knows exactly what comes back. It does not distinguish itself from siblings like get_fitness or get_daily_health, which likely overlap on training/health data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The only usage cue is 'Pick via `metrics`', which is a parameter instruction rather than when-to-use guidance. Nothing says when to call this instead of get_fitness or get_daily_health, nor any prerequisite or exclusion.

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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