ham.run

Caffeine + nicotine pharmacokinetic timing

caffeine_protocol

Read-onlyIdempotent

Model caffeine (and optional nicotine) blood concentration, performance gain, and side-effect curves over a race using a 1-compartment oral PK model. Source: ham.run ergogenic module. Pass useMyData:true to overlay body weight from the connected athlete profile.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`nicOn`	Yes	Enable nicotine co-administration model.
`nicForm`	Yes	Nicotine delivery form.	gum
`finishMin`	Yes	Expected race duration in minutes.
`nicDoseMg`	Yes	Nicotine dose in mg.
`useMyData`	No	Overlay body weight from the connected athlete profile.
`bodyWeightKg`	No	Body weight in kilograms.
`cafTimingMin`	Yes	Minutes BEFORE race start that caffeine is taken.
`nicTimingMin`	Yes	Minutes before race start for nicotine.
`extraCafDoses`	Yes	In-race top-up doses, e.g. with gels.
`cafDoseMgPerKg`	Yes	Pre-race caffeine dose, mg/kg. 3–6 mg/kg is the evidence-based range.

Output Schema

TableJSON Schema

Name	Required	Description
`input`	Yes	Echo of resolved input parameters.
`giRisk`	Yes	GI distress risk level.
`timeSavedMin`	Yes	Estimated time saved, minutes.
`hrElevationBpm`	Yes	Expected HR elevation from caffeine, bpm.
`perfCheckpoints`	Yes	Performance checkpoints at ~10 min intervals.
`totalCaffeineMg`	Yes	Total caffeine consumed, mg.
`peakRpeReduction`	Yes	Peak RPE reduction from ergogenic effect.
`caffeineDoseEvents`	Yes	Timestamped dose events.
`avgPerformanceGainPct`	Yes	Average performance gain over race duration, %.

TDQS

A4.4/5.0

Behavior4/5

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

Beyond the read-only and idempotent annotations, the description adds the model type (1-compartment oral PK), source (ham.run ergogenic module), and a usage tip for useMyData. It also discloses that it outputs concentration, performance, and side-effect curves, which is useful behavioral context.

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 three sentences, front-loaded with the main action, and each sentence adds meaningful context: the model purpose, the source, and a parameter hint. No unnecessary words.

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?

For a 10-parameter tool with an output schema, the description covers the core purpose, the optionality of nicotine, and the data overlay option. It doesn't explain all parameters, but the schema and output schema fill those gaps, making this sufficiently complete.

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 extra meaning by explaining the useMyData parameter and noting that nicotine is optional, which helps clarify the boolean nicOn and the related dosing parameters.

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 models caffeine and optional nicotine blood concentration, performance gain, and side-effect curves using a 1-compartment oral PK model. This is a specific verb-resource pairing that distinguishes it from siblings like fueling_plan or pacing_strategy.

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 implies usage for simulating ergogenic effects over a race and mentions the useMyData option to pull body weight from an athlete profile. It does not explicitly exclude alternatives or state when not to use it, but the context is clear.

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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