garmin-connect-mcp

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schedule_workout

Schedule an existing workout on your Garmin calendar by providing the workout ID and desired date, keeping your training organized.

Instructions

Put an existing workout plan on the Garmin training calendar.

Args: workout_id: The numeric id, from list_workouts or create_workout. day: The date to schedule it on, for example "tomorrow" or "2026-08-20".

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": {
      "workout_id": "<workout_id>",
      "day": "<day>"
    },
    "name": "schedule_workout"
  }
}

Input Schema

Name	Required	Description	Default
`day`	Yes
`workout_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 18, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the action (putting on calendar) without mentioning side effects, permissions, overwrite behavior, error handling, or idempotency. For a mutation tool, this is insufficient transparency.

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 extremely concise with three sentences, including a clear purpose statement and parameter explanations. Every sentence earns its place without any fluff, making it easy to scan and understand.

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?

Given the presence of an output schema, return values are covered. However, the description lacks details on error conditions (e.g., scheduling an already occupied day), overwriting behavior, or prerequisites beyond the workout_id. It is adequate but not fully complete for a scheduling mutation 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%, but the description adds valuable context: workout_id is explained as 'The numeric id, from list_workouts or create_workout,' and day includes usage examples ('tomorrow' or '2026-08-20'). This compensates well for the lack of schema descriptions, providing meaning beyond the raw types.

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 clearly states 'Put an existing workout plan on the Garmin training calendar,' using a specific verb (Put) and resource (existing workout plan onto calendar). It distinguishes from sibling tools like create_workout (creates plans) and get_scheduled_workouts (lists scheduled), establishing a unique purpose.

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

Usage Guidelines3/5

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

The description implicitly suggests when to use the tool by referencing where to get the workout_id (list_workouts or create_workout), but it does not explicitly state when to use vs alternatives, nor does it provide exclusions or context for when not to use it. This leaves the agent needing to infer usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.