garmin-mcp

get_progress_summary

Calculate total distance, duration, elevation gain, or moving time for any activity type between two dates. Track your training progress over a custom date range.

Instructions

Totals per activity type between two dates: distance, duration, elevation gain or moving time.

Use for "how many km did I run this month" or "hours of cycling this year".

Args: start_date: YYYY-MM-DD. Defaults to 30 days ago. end_date: YYYY-MM-DD. Defaults to today. metric: One of distance, duration, movingDuration, elevationGain, elevationLoss.

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_progress_summary"
  }
}

Input Schema

Name	Required	Default
`metric`	No	distance
`end_date`	No
`start_date`	No

Output Schema

Name	Required	Description
`data`	No	Compacted Garmin payload; null if unavailable.
`note`	No	Explains missing data or truncation.
`params`	No	Parameters used.
`source`	Yes	Garmin Connect API method the data came from.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 24, 2026

TDQS

A4.2/5.0

Behavior3/5

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

No annotations are provided, so the description must carry behavioral disclosure. It states the aggregation logic and default date behavior, but does not mention edge cases like empty results, date inclusivity, or data completeness. Adequate but not rich.

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 compact and front-loaded with the core purpose, followed by concrete examples and a clear Args list. Every sentence earns its place with no redundancy.

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?

An output schema exists, so the description doesn't need to explain return values. It covers purpose, usage, parameter formats, and examples. The main gap is not addressing edge cases or explicit grouping behavior, but it is sufficient for a simple summary tool.

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?

Schema description coverage is 0%, so the description compensates well. The Args section provides formats (YYYY-MM-DD), defaults (30 days ago, today), and enumerates the valid metric values (distance, duration, movingDuration, elevationGain, elevationLoss), adding meaning the 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?

The description states 'Totals per activity type between two dates: distance, duration, elevation gain or moving time.' This names a specific verb and resource, and clearly distinguishes it from sibling get_* tools by specifying aggregation by activity type and date range.

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?

Provides explicit use cases ('how many km did I run this month', 'hours of cycling this year') that clearly indicate when to call this tool. It does not name alternatives or exclusions, but the context is precise enough to select it.

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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