garmin-mcp

get_scheduled_workouts

Retrieve a monthly training calendar from Garmin Connect, listing scheduled workouts, completed activities, and plan items. Provide a year and month to view your training plan.

Instructions

Training calendar for a month: scheduled workouts, completed activities, plan items.

Args: year: Four-digit year. Defaults to the current year. month: 1-12. Defaults to the current month.

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_scheduled_workouts"
  }
}

Input Schema

Name	Required	Description	Default
`year`	No
`month`	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

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the behavioral burden. It does disclose useful behavior such as defaulting year/month to the current values and returning a month-scoped calendar. However, it does not clarify timezone handling, whether future months include completed items, or whether this is purely read-only, though the operation is clearly non-destructive by nature.

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 well-structured: a one-line purpose statement followed by terse, informative parameter definitions. Every sentence earns its place and the most important information is front-loaded.

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 simple read-only query with two optional parameters and an output schema, the description is mostly complete. It explains the purpose and all parameter semantics, though a brief note distinguishing it from list_workouts would make it fully unambiguous for tool selection.

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 coverage is 0%, so the description must explain the parameters, and it does. It defines year as a four-digit integer defaulting to the current year and month as 1-12 defaulting to the current month. This adds real meaning beyond the bare schema.

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 clearly identifies the resource as a month-long training calendar and lists the content categories returned (scheduled workouts, completed activities, plan items). It is understandable and specific, though it does not differentiate itself from sibling tools like list_workouts or schedule_workout.

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?

No explicit guidance is given about when to use this tool versus alternatives such as list_workouts or get_recent_activities. The description implies monthly calendar use, but it never states exclusions or selection criteria, so the agent must infer the appropriate context.

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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