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Garmin MCP Server

Garmin MCP Server

by JJRPF
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_menstrual_calendar_data

Retrieve menstrual calendar data for a date range. Automatically handles Garmin's 92-day limit by chunking requests and merging results.

Instructions

Get menstrual calendar data between specified dates

Automatically chunks requests longer than 92 days, Garmin's server-side limit, and stitches the responses together.

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_menstrual_calendar_data"
  }
}

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

A3.7/5.0
Behavior3/5

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

The description discloses a key behavioral trait: it chunks long requests and stitches responses, indicating internal handling of pagination or limits. It does not mention return format, potential errors, or whether it is read-only (though 'Get' implies so), leaving some behavioral aspects unspecified.

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, consisting of two sentences that deliver the primary purpose and a relevant behavioral note. No unnecessary words or redundant details, making it highly efficient.

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?

Given that an output schema exists (as noted in context), the description does not need to explain return values. It adequately covers the essential usage context, including the chunking behavior for long date ranges. Minor details like date inclusivity are omitted but not critical for basic use.

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?

The description adds crucial meaning to both parameters by specifying 'YYYY-MM-DD format' for start_date and end_date, which the schema lacks. This clarifies the expected input format and resolves potential ambiguity.

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 states the action ('Get') and resource ('menstrual calendar data') with a specific date range. It does not explicitly contrast with the sibling tool 'get_menstrual_data_for_date', but the range vs. single-date distinction is implicit, making the purpose understandable without ambiguity.

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 provides a guideline about automatic chunking for requests over 92 days, which helps with long ranges. However, it does not mention when to prefer this tool over the single-date alternative or any other usage context, relying on the user to infer the appropriate scenario.

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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