ham.run

Generate a week-by-week marathon training plan

generate_training_plan

Read-onlyIdempotent

Build a concrete marathon training schedule from a periodization model: per-week sessions (long run, intervals, tempo, easy runs) with distances and prescribed paces derived from current fitness via the Riegel power law. Optionally anchored to a race date for calendar dates. Source: ham.run training plan module.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`age`	Yes	Age in years.
`sex`	No	Athlete sex — affects VO₂max ceiling and adaptation curves.
`model`	Yes	Training intensity distribution model.	pyramidal
`raceDate`	No	Race date (YYYY-MM-DD). When set, sessions get calendar dates and the race lands in the final week.
`longRunDay`	Yes	Day of the week for the long run.	Sun
`totalWeeks`	Yes	Plan length in weeks. If raceDate is set, the race lands in the final week — choose totalWeeks ≈ weeks until the race.
`daysPerWeek`	Yes	Run days per week.
`weeklyVolumeKm`	Yes	Peak training volume, km/week.
`startingMarathonTime`	Yes	Current marathon PR / fitness baseline.

Output Schema

TableJSON Schema

Name	Required	Description
`input`	Yes	Echo of resolved input parameters.
`model`	Yes	Periodization model name.
`weeks`	Yes	Week-by-week schedule.
`summary`	Yes
`citation`	Yes	Academic citation for the TID model.
`goalPace`	Yes	Goal marathon pace, min/km.
`goalTime`	Yes	Predicted finish time after the plan.

TDQS

A4/5.0

Behavior4/5

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

Annotations declare readOnly, idempotent, and non-destructive. The description adds value by detailing the methodology (Riegel power law, periodization) and optional race date anchoring, which are behavioral aspects beyond the annotations. No contradictions.

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 front-load key information: what it builds, method, and optional feature. Every word adds value, no redundancy.

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 9 parameters, 7 required, and an output schema, the description covers the output (weekly sessions with distances/paces), methodology, and optional race date. It could mention prerequisites or expected user context, but is adequate for a plan generator.

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?

Schema coverage is 100% with detailed parameter descriptions. The description provides high-level context but does not significantly extend the semantics of individual parameters beyond what the schema already offers. For example, 'model' schema says 'Training intensity distribution model' and description doesn't elaborate.

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 it builds a marathon training schedule from a periodization model, specifying key components like sessions, distances, and paces. It uses a specific verb ('Build') and resource ('training schedule'), and given the sibling tools (e.g., pacing_strategy, periodization_compare), it uniquely describes full plan generation.

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

Usage Guidelines3/5

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

The description implies when to use (generate a concrete training plan) but does not explicitly provide when-not-to-use or compare with alternatives like periodization_compare or pacing_strategy. The optional race date anchoring is mentioned but no exclusion criteria.

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.

Resources

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