Calcgrid

BMR Calculator

calculate_bmr

Read-onlyIdempotent

Use this when you need resting daily calorie needs for an adult from sex, weight, height and age, as a base for diet or TDEE calculations. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery.

Do not use this when you need total daily calories including activity (use tdee), the person is a child, pregnant, or has very high muscle mass (use a body-fat based equation such as Katch-McArdle instead). What it computes: Estimates basal metabolic rate (calories burned at complete rest per day) with the Mifflin-St Jeor equation, and also reports the revised Harris-Benedict estimate. Inputs: sex (enum); weight_kg (number, kg); height_cm (number, cm); age_years (number, years). Complete JSON argument examples: {"sex":"male","weight_kg":70,"height_cm":175,"age_years":30} | {"sex":"female","weight_kg":60,"height_cm":165,"age_years":40} Outputs: bmr_kcal_per_day [kcal/day], harris_benedict_kcal_per_day [kcal/day], bmr_kj_per_day [kJ/day]. Formula: Mifflin-St Jeor: BMR = 10·weight_kg + 6.25·height_cm − 5·age_years + 5 (male) or − 161 (female). Harris-Benedict (1984): male 88.362 + 13.397·W + 4.799·H − 5.677·A; female 447.593 + 9.247·W + 3.098·H − 4.330·A. Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/bmr with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/health/bmr.md

Input Schema

TableJSON Schema

Name	Required	Description
`sex`	Yes	Biological sex used to select the formula coefficients.
`age_years`	Yes	Age in years (adults only). Unit: years.
`height_cm`	Yes	Height in centimetres. Unit: cm.
`weight_kg`	Yes	Body weight in kilograms. Unit: kg.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`links`	No
`result`	Yes
`request`	Yes
`sources`	No
`success`	Yes
`version`	No
`freshness`	No
`timestamp`	Yes
`next_actions`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 26, 2026

addedInput schema / examples

Added value: +[
+  {
+    "age_years": 30,
+    "height_cm": 175,
+    "sex": "male",
+    "weight_kg": 70
+  },
+  {
+    "age_years": 40,
+    "height_cm": 165,
+    "sex": "female",
+    "weight_kg": 60
+  }
+]

AddedSep 25, 2026
RemovedSep 25, 2026
First observedSep 25, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already cover readOnly, idempotent, non-destructive traits. The description adds significant behavioral detail: the exact equations used (Mifflin-St Jeor and revised Harris-Benedict), the output fields, and even a fallback REST endpoint with explicit instructions not to guess other paths. This far exceeds what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear section headings (Inputs, Outputs, Formula, Direct REST fallback). Every sentence adds value, though it could be trimmed slightly by omitting the full formulas, which are unlikely to be needed by an agent for calling the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich schema and output schema, the description provides everything an agent needs: exact inputs, outputs, examples, a fallback path, and documentation link. No critical information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters with descriptions and units, but the description adds a compact parameter list with types and units, plus two complete JSON examples. This reinforces and clarifies the schema without redundancy.

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 states a clear, specific purpose: estimating resting daily calorie needs for adults from sex, weight, height, and age. It explicitly contrasts with sibling tools like calculate_tdee, making its scope unambiguous.

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

Usage Guidelines5/5

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

It provides explicit when-to-use (base for diet or TDEE) and when-not-to-use guidance (children, pregnancy, high muscle mass) with named alternatives (tdee, Katch-McArdle). This is exemplary routing.

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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