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Get weekly volume + CTL/ATL/TSB

get_training_load
Read-only

Return per-week distance, time-in-zone, and rolling chronic/acute training load (CTL, ATL, TSB) for the signed-in athlete. TSS is estimated from HR (avg_hr / threshold_hr clamped), so values are useful for trends but not directly comparable to power-based TSS. Requires authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weeksYesNumber of weeks of history to analyse.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
weeksYesPer-week volume and zone distribution.
caveatsYesWarning if threshold HR was not available.
fitnessYesFitness/fatigue model output.
windowWeeksYesWeeks of data returned.
thresholdHrUsedYesThreshold HR used for TSS estimation.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds value by disclosing the HR-based TSS estimation method and the authentication requirement, which are behavioral traits not visible in the schema or annotations.

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 sentences: the first states the core function, the second adds a critical methodological caveat. Every sentence is informative, and the structure front-loads the main purpose.

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?

With an output schema, one well-documented parameter, and annotations, the description covers the essential aspects: purpose, metrics, methodology, and authentication. It lacks details on edge cases like insufficient data, but that is not essential for this straightforward read-only tool.

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?

The schema provides 100% coverage for the single 'weeks' parameter, including type, default, min/max, and a clear description. The tool description does not need to add parameter details; the baseline of 3 applies since the schema carries the explanatory burden.

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 the tool returns per-week distance, time-in-zone, and CTL/ATL/TSB for the signed-in athlete. This specific verb-resource-scope combination distinguishes it from sibling tools like get_recent_activities, which likely returns raw activities rather than aggregated load metrics.

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 provides context by noting TSS is estimated from HR and not directly comparable to power-based TSS, implying caution for power-specific analyses. It does not name alternative tools explicitly, but the caveat offers meaningful usage guidance about when the results are appropriate.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct aspect of endurance running performance: caffeine modeling, fueling, pacing, athlete data, activities, training load, heat acclimation, pacing strategy, periodization, race prediction, and running economy. No overlap.

Naming Consistency3/5

Names use snake_case but mix verb_noun (e.g., get_my_athlete, predict_race_time), noun_verb (periodization_compare), and pure noun (caffeine_protocol, fueling_plan). Inconsistent pattern but still understandable.

Tool Count5/5

11 tools cover the domain of running performance modeling without being too many or too few. Each tool serves a clear purpose within the server's scope.

Completeness4/5

Covers most core areas: personal data retrieval, activity history, training load, and multiple performance models. Minor gaps like workout creation or nutrition beyond fueling plan, but the surface is largely complete for modeling.

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