Free marathon performance tools: running economy, periodization, cardiac drift, substrate modeling, race prediction, and more.
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Last probed Nov 3, 2026 · mcp.ham.run
13tools discovered
For AI agents — MCPBundles Directory API
For MCP client apps (human connect):
MCP connect URL: https://mcp.ham.run/mcp
Skill page: https://www.mcpbundles.com/skills/ham-mcp-bea67dd8af
Explore capabilities (13)
read 13
Get
3
Generate
2
Other capabilities
8
Predict race times
read
predict_race_time
Predict times across 5K / 10K / Half / Marathon from a known race result, using a distance-varying Riegel exponent (1.06 up to 10K, 1.07 to the half, 1.08 to the marathon) and age-graded performance scores against world records. Predictions are point estimates with no confidence interval — treat a marathon predicted from a short race as an optimistic ceiling. Source: ham.run race predictor. Pass useMyData:true to overlay age + sex from the connected athlete profile.
Predict race times — Predict times across 5K / 10K / Half / Marathon from a known race result, using a distance-varying Riegel exponent (1.06 up to 10K, 1.07 to the half, 1.08 to the marathon) and age-graded performance scores against world records. Predictions are point estimates with no confidence interval — treat a marathon predicted from a short race as an optimistic ceiling. Source: ham.run race predictor. Pass useMyData:true to overlay age + sex from the connected athlete profile.
Fueling and time-to-exhaustion — 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.
Marathon pacing — cardiac drift, hydration, sodium — Simulate marathon HR drift and fluid/sodium balance under given heat, sweat, and aid-station conditions. Returns total drift split by cause, decoupling %, kilometre at LT₂ breach, the kilometre where holding pace would demand more than HRmax, plasma sodium, and body-weight loss. Pace at the target %HRR is derived from the athlete's own VO₂max and cost of running, so supply marathonPr or vo2max for a pace specific to them. Source: ham.run HR pacing module. Pass useMyData:true to overlay age + res
Caffeine + nicotine pharmacokinetic timing — 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.
Running economy (Cr) calculator — Compute oxygen cost of running (Cr in mL O₂·kg⁻¹·m⁻¹) given pace, gradient, surface, shoe type, and athlete characteristics. Returns full multiplicative breakdown. Source: ham.run running-economy module (Barnes & Kilding 2015 + Minetti 2002).
Compare 16-week training plan models — Forecast race-time progression across pyramidal, polarized, and threshold periodization models for a given weekly volume and starting marathon time. Returns weekly evolution of VO₂max, running economy, LT₂, and predicted finish. Source: ham.run periodization module.
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Free marathon performance tools: running economy, periodization, cardiac drift, substrate modeling, race prediction, and more.
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How many tools does HAM.RUN Performance Lab provide?
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Other MCP servers in this category from the directory index
Sauna / heat acclimation forecast — Predict plasma volume expansion, VO₂max gain, race-time improvement, growth-hormone response, and overtraining risk for a sauna heat-acclimation protocol. Source: ham.run sauna module (Scoon 2007, Kirby 2021).
Grade-adjusted pace splits — Distribute a goal time across a course profile in proportion to each segment's Minetti gradient cost. Returns per-mile or per-km splits with pace, elevation gain/loss, and average grade. Source: ham.run GAP module.
Generate a week-by-week marathon training plan — 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.
Generate a single structured running workout — Produce one concrete running session from the desired stimulus (recovery, endurance, marathon pace, threshold, VO2max, speed, or mixed) and training phase, scaled to fitness anchored by any recent race time via the Riegel power law. Prescriptions follow Daniels-style time-domain quality windows converted to track-friendly distances at the athlete’s own paces. Deterministic for a given seed — vary the seed for a different workout of the same type. Source: ham.run workout generator module.
Get the connected athlete profile — 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.
Get the athlete's recent runs — List the signed-in athlete's recent activities with date, distance, duration, pace, average HR, and HR-zone classification. Use to ground training-status questions ("how was my last week?", "did I overdo it?"). Requires authentication.
Get weekly volume + CTL/ATL/TSB — 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.
MCPBundles probed 13 tools on the live server. The tool list on this page reflects what was discovered at the last refresh — connect your client to see the full set available to your session.
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