ham.run

Fueling and time-to-exhaustion

fueling_plan

Read-onlyIdempotent

Compute substrate partitioning (CHO/fat oxidation), required carb intake rate, and predicted time-to-glycogen-depletion for an endurance effort. Source: ham.run substrate module. Pass useMyData:true to overlay weight and VO₂max from the connected athlete profile.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sex`	No	Athlete sex — affects substrate partitioning.
`vo2max`	No	Estimated VO₂max in mL O₂ · kg⁻¹ · min⁻¹.
`weightKg`	No	Body weight in kilograms.
`pctVO2max`	Yes	Race intensity as % VO₂max.
`useMyData`	No	Overlay weight and VO₂max from the connected athlete profile.
`glycogenPoolG`	Yes	Starting muscle+liver glycogen, grams. Typical 400–700.
`transportType`	Yes	"sglt1" caps absorption at 1.0 g/min; "dual" (glucose+fructose) at 1.5 g/min.	dual
`choIntakeGPerHr`	Yes	Planned CHO intake during race, g/hr.

Output Schema

TableJSON Schema

Name	Required	Description
`rer`	Yes	Respiratory exchange ratio at given intensity.
`input`	Yes	Echo of resolved input parameters.
`notes`	Yes	Warning if intake exceeds absorption ceiling.
`vo2LPerMin`	Yes	Absolute VO₂ at race intensity, L/min.
`choEnergyPct`	Yes	Percentage of energy from carbohydrate.
`energyKJPerMin`	Yes	Total energy expenditure, kJ/min.
`timeToExhaustion`	Yes	Formatted time to exhaustion.
`choOxidationGPerMin`	Yes	Carbohydrate oxidation rate, g/min.
`fatOxidationGPerMin`	Yes	Fat oxidation rate, g/min.
`timeToExhaustionMin`	Yes	Minutes until glycogen depletion.
`effectiveIntakeGPerMin`	Yes	Effective CHO intake after absorption cap, g/min.
`recommendedIntakeGPerHr`	Yes	Recommended CHO intake, g/hr.
`absorptionCeilingGPerMin`	Yes	Max gut absorption rate, g/min.

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context beyond this: it mentions the source module ('ham.run substrate module') and explains the overlay behavior with useMyData. This provides useful information without contradicting the 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 sentences with no filler. The first sentence states the core outputs, the second adds a source reference and a key parameter tip. Every word earns its place, and the most important information is front-loaded.

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?

Given the 8 parameters with full schema descriptions and an output schema, the description effectively conveys the tool's purpose and the one special behavior (useMyData). It could mention limitations or assumptions of the model, but it is not incomplete for an AI agent to select and invoke the tool.

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 meaning by connecting parameters to the computation's purpose and specifically explaining the useMyData overlay behavior, reinforcing and contextualizing the schema's parameter descriptions.

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 names a specific verb ('Compute') and resource ('substrate partitioning', 'required carb intake rate', 'predicted time-to-glycogen-depletion') for an 'endurance effort'. This clearly distinguishes it from sibling tools like pacing_strategy or predict_race_time, which focus on other performance aspects.

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 states the context ('for an endurance effort') and gives a specific tip for using useMyData. It does not explicitly mention alternatives or when not to use, but the purpose is clear enough to infer appropriate usage relative to siblings.

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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