Calories Burned Grounding Data

Overview

Activities and MET values come from the 2024 Adult Compendium of Physical Activities (Herrmann SD, Willis EA, Ainsworth BE, et al. 2024 Adult Compendium of Physical Activities: A third update of the energy costs of human activities. J Sport Health Sci. 2024;13(1):6-12; https://pacompendium.com). The Compendium is free for commercial use with citation. A monthly collector copies its activity tables into our storage; MET values are stored and returned exactly as published.

Calories per minute are `met x 3.5 x weightKg / 200`: one MET is 3.5 ml of oxygen per kilogram per minute and a litre of oxygen releases about 5 kcal. `caloriesPerHour` and `totalCalories` are rounded to whole kcal. `intensity` classifies the standard MET with the Physical Activity Guidelines bands: light under 3, moderate 3 to 5.9, vigorous 6 and above, with 1.5 or below reported as sedentary.

With `age`, `sex` and `height` (all three) the API estimates resting metabolic rate with Mifflin-St Jeor (10 x kg + 6.25 x cm - 5 x age, plus 5 for men or minus 161 for women); `rmr` replaces the estimate with a measured value. That rate is converted to ml O2/kg/min and `correctedMet = met x 3.5 / RMR`, the Compendium's corrected MET method; calories then use the corrected value. `minutesToBurn` is `calories` divided by calories per minute. Search ranks activities that contain every word of the query (after synonyms such as biking to bicycling), preferring general and non-competitive entries, and falls back to partial matches.

Live Test Calories Burned Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as CaloriesBurnedGroundingData. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "CaloriesBurnedGroundingData",
  "arguments": {}
}

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "query": "running",
    "count": 5,
    "totalMatches": 73,
    "weightKg": 70,
    "durationMinutes": 30,
    "targetCalories": null,
    "personalized": false,
    "rmrKcalPerDay": null,
    "activities": [
      {
        "code": "12145",
        "activity": "Running, self-selected pace",
        "heading": "Running",
        "met": 10.5,
        "correctedMet": null,
        "intensity": "vigorous",
        "caloriesPerHour": 772,
        "durationMinutes": 30,
        "totalCalories": 386,
        "minutesToBurn": null
      },
      {
        "code": "12140",
        "activity": "Running, cross country",
        "heading": "Running",
        "met": 9.3,
        "correctedMet": null,
        "intensity": "vigorous",
        "caloriesPerHour": 684,
        "durationMinutes": 30,
        "totalCalories": 342,
        "minutesToBurn": null
      },
      {
        "code": "12170",
        "activity": "Running, stairs, up",
        "heading": "Running",
        "met": 15,
        "correctedMet": null,
        "intensity": "vigorous",
        "caloriesPerHour": 1103,
        "durationMinutes": 30,
        "totalCalories": 551,
        "minutesToBurn": null
      },
      {
        "code": "12150",
        "activity": "Running (Taylor Code 200)",
        "heading": "Running",
        "met": 8,
        "correctedMet": null,
        "intensity": "vigorous",
        "caloriesPerHour": 588,
        "durationMinutes": 30,
        "totalCalories": 294,
        "minutesToBurn": null
      },
      {
        "code": "12180",
        "activity": "Running, on a track, team practice",
        "heading": "Running",
        "met": 10,
        "correctedMet": null,
        "intensity": "vigorous",
        "caloriesPerHour": 735,
        "durationMinutes": 30,
        "totalCalories": 368,
        "minutesToBurn": null
      }
    ]
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at query: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 1 credit each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Workout Logging Apps

Fitness apps turn a logged activity and duration into calories burned, showing the MET and intensity class so users see why a session counted the way it did.

Calorie Goal Planning

Nutrition coaches use the reverse mode to tell clients how many minutes of walking, cycling or swimming offset a snack or meal.

Corporate Wellness Challenges

Step and activity challenges convert mixed activities, from yard work to basketball, into one comparable calorie or MET-minute total.

Wearable and Health Platforms

Device makers fill in activities their sensors cannot classify with Compendium-based estimates personalised to the wearer's age, sex, height and weight.

Other ways to use Calories Burned Grounding Data

Set up Calories Burned Grounding Data on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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