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Get the connected athlete profile

get_my_athlete
Read-only

Returns the signed-in athlete's profile (age, sex, weight, VO₂max, marathon PR, biomechanics) plus derived HR zones. Use this once at the start of a coaching conversation so subsequent calculations can be personalised. Requires authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoData source identifier.
messageNoGuidance message if no profile exists.
profileYesAthlete profile (age, sex, weight, VO₂max, marathon PR, biomechanics) or null.
derivedZonesNoHR training zones derived from profile.

TDQS

A4.7/5.0
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 safe read nature is known. The description adds the behavioral detail that authentication is required, which is beyond the annotations. It also implies that the profile is personal to the signed-in athlete, adding context. No contradictions with 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?

Two concise sentences. The first states the core function and the data fields; the second gives usage timing and an authentication note. Every sentence adds value, with no filler. The most critical information is front-loaded.

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?

The tool is simple with no parameters, and an output schema exists to describe the return structure. The description covers purpose, when to use it, required authentication, and the scope of data. No additional context is needed for an agent to select and invoke this tool correctly.

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 tool has zero parameters, so the schema coverage is trivially 100% and the baseline is 4. The description does not need to explain parameters; instead, it usefully lists the data fields returned, which helps the agent understand what the tool provides without needing the output schema.

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 uses the specific verb 'Returns' and clearly states the resource: the signed-in athlete's profile, including specific data fields like age, sex, weight, VO₂max, marathon PR, biomechanics, and derived HR zones. This unambiguously distinguishes it from sibling tools which focus on planning or pacing.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use this once at the start of a coaching conversation so subsequent calculations can be personalised.' It also notes the authentication requirement, which is a key prerequisite. No alternative tool is mentioned because none exists among the siblings for this purpose, making the guidance complete.

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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