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epodivilov

garmin-connect-mcp

by epodivilov

get_training_stress_balance

Retrieve training stress balance (TSB) to evaluate form and freshness from chronic and acute training load data for any selected date.

Instructions

Get training stress balance (TSB), chronic training load (CTL), and acute training load (ATL) for a specific date. TSB = CTL - ATL indicates form/freshness. Uses HR-based TSS calculation when available, falls back to duration estimates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoTarget date in YYYY-MM-DD format (defaults to today)
daysNoNumber of days of historical data to analyze (default: 90, min: 7, max: 365)
includeTimeSeriesNoInclude daily time series data showing TSS, CTL, ATL, TSB progression (default: true)
includeSummaryOnlyNoReturn only summary data without time-series (default: false)
summaryNo[DEPRECATED: Use includeSummaryOnly] Return only summary data without time-series (default: false)
restingHRNoCustom resting heart rate for TSS calculation (default: 50 bpm)
maxHRNoCustom maximum heart rate for TSS calculation (default: 185 bpm)
thresholdHRNoCustom threshold heart rate for TSS calculation (default: 90% of maxHR)
Behavior4/5

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

Despite no annotations, the description discloses the core behavior: TSB calculation, reliance on HR-based TSS with fallback to duration estimates. It does not cover potential side effects or auth needs, but the read-only nature is clear and the calculation logic is transparent.

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?

Two sentences that front-load the key outputs (TSB, CTL, ATL) and provide a concise explanation. Every word is meaningful, with no repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 optional parameters, no output schema, and no annotations, the description adequately covers the tool's functionality, calculations, and fallback behavior. An AI agent can determine when to use it and what to expect.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the TSB formula and fallback calculation, which goes beyond the parameter descriptions. For example, it clarifies how the parameters like HR settings relate to the output metrics.

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 identifies the tool as retrieving training stress balance (TSB), chronic training load (CTL), and acute training load (ATL) for a specific date, and explains the formula TSB = CTL - ATL. This distinguishes it from siblings like get_activities or get_workout_details, which serve different purposes.

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

Usage Guidelines4/5

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

The description effectively communicates the tool's use case (assessing training form/freshness via TSB) and explains the calculation methods. While it doesn't explicitly state when not to use it or list alternatives, the sibling tools are sufficiently disparate that confusion is unlikely.

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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