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Samsung Health Daily Summary

samsung_health_daily_summary
Read-onlyIdempotent

Generate a daily wellness summary from local Samsung Health export data, aggregating activity, sleep, heart, and stress metrics while keeping data private.

Instructions

Build a daily wellness summary from local Samsung Health export data. Individual workout records follow privacy_mode (default summary: aggregates only). It is not live Samsung Health and not medical advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoYYYY-MM-DD local date. Defaults to today in the configured timezone.
timezoneNoIANA timezone, e.g. America/Fortaleza. Defaults to SAMSUNG_HEALTH_TIMEZONE or UTC.
privacy_modeNo
response_formatNomarkdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.7.4
    • addedInput schema / properties / privacy_mode
      Added value: +{
      +  "enum": [
      +    "summary",
      +    "structured",
      +    "raw"
      +  ],
      +  "type": "string"
      +}
  2. First observedv0.5.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context: it clarifies that data is local, not live, not medical advice, and that privacy_mode defaults to 'summary' which aggregates individual records. This goes beyond the annotation safety profile.

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 two concise sentences. The primary purpose is front-loaded, and the caveats are placed efficiently. Every sentence adds value without redundancy.

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 covers the main purpose and privacy behavior but does not specify the output format or what 'structured' and 'raw' privacy modes produce. Since there is no output schema, the agent might be uncertain about return shapes. This is a notable gap for a tool with multiple enums.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (date and timezone have descriptions; privacy_mode and response_format do not). The description explains privacy_mode's effect ('aggregates only') but does not elaborate on response_format or the 'structured'/'raw' options. It partially compensates for the schema gap but leaves some parameters under-explained.

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 a specific action ('Build a daily wellness summary') and a clear resource ('from local Samsung Health export data'). It also distinguishes itself from live Samsung Health and from sibling tools like weekly_summary, making the purpose unambiguous.

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 implies usage for daily summaries but does not explicitly contrast with weekly_summary or other tools. It doesn't state when not to use it or mention alternatives. The context is implied rather than explicit, which is adequate but not fully guiding.

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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