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Garmin MCP Server

Garmin MCP Server

by JJRPF
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

add_hydration_data

Record water consumption in milliliters with a date and timestamp to log hydration data for accurate daily fluid tracking.

Instructions

Add hydration data

Args: value_in_ml: Amount of liquid in milliliters cdate: Date in YYYY-MM-DD format timestamp: Timestamp in YYYY-MM-DDThh:mm:ss.sss 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": {
      "value_in_ml": "<value_in_ml>",
      "cdate": "<cdate>",
      "timestamp": "<timestamp>"
    },
    "name": "add_hydration_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
cdateYes
timestampYes
value_in_mlYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
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 burden. It only states 'Add hydration data' without disclosing any side effects (e.g., idempotency, overwrite behavior, required authentication) beyond the basic action.

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 and free of any unnecessary wording. It includes the essential action and parameter explanations in a compact format.

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?

The description is adequate for a simple mutation, but lacks broader context such as the purpose of the hydration data, expected response format, or error handling. The parameter explanations are useful, but the overall context is minimal.

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 provides meaningful details for all parameters: units for value_in_ml and exact formats for cdate and timestamp. This adds value over the bare schema, which only lists parameter names and types.

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 ('Add hydration data') with a specific verb and object. It distinguished from the sibling 'get_hydration_data' tool, but does not elaborate on the exact resource or context (e.g., logging a new entry).

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 on when to use this tool versus alternatives. The existence of a complementary 'get_hydration_data' tool implies usage for adding, but the description does not state this or mention any conditions.

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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