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mrslbt

tabedata-mcp

by mrslbt

Calculate Personalized Daily Targets

daily_targets
Read-onlyIdempotent

Calculate personalized daily calorie and macronutrient targets based on your body metrics, activity level, and goals. Adjusts macros for diabetes risk and provides sodium guidance for hypertension.

Instructions

Calculate personalized daily calorie, protein, carb, fat, fiber, and water targets. Uses Mifflin-St Jeor for BMR, an activity multiplier for TDEE, and a goal-driven deficit/surplus. Diabetes risk shifts the macro split toward fat (35%) to lower carb load. Hypertension surfaces a sodium guidance note.

個別の1日あたりカロリー・PFC・繊維・水分量を算出します。BMR は Mifflin-St Jeor、TDEE は活動係数、目標から赤字・黒字を決定。糖尿病リスクがある場合は脂質比率を高めて糖質負荷を抑えます。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageYesAge in years, e.g. 30. 年齢。
sexYesBiological sex (used by the BMR formula). 性別。
goalYesBody composition goal. 目標。
activityYesActivity level. 活動レベル。
height_cmYesHeight in centimeters, e.g. 175. 身長(cm)。
weight_kgYesBody weight in kilograms, e.g. 70. 体重(kg)。
conditionsNoOptional health conditions. Adjusts macro split (diabetes_risk shifts toward fat) and surfaces guidance notes (hypertension flags sodium ceiling). 持病。
Behavior4/5

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

Annotations indicate readOnlyHint=true and idempotentHint=true, signaling safe, deterministic behavior. The description adds beyond annotations by detailing the calculation method (Mifflin-St Jeor, activity multiplier) and condition-specific adjustments (diabetes risk shifts fat to 35%, hypertension adds sodium note). 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?

The description is two concise sentences in English and Japanese, front-loaded with the core function. Every sentence provides essential information—calculation method, condition effects—with no redundant or filler content.

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 7 parameters (6 required), no output schema, but strong annotations, the description covers calculation logic and condition adjustments adequately. It does not specify return format or error handling, but this is acceptable for a read-only calculation tool.

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 description coverage is 100%, with descriptions for all 7 parameters. The description adds value by specifying that 'diabetes_risk' shifts macros toward 35% fat, providing concrete detail not in the schema. This adds meaning beyond the basic parameter names.

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 explicitly states it calculates personalized daily targets for calories, protein, carbs, fat, fiber, and water using Mifflin-St Jeor, activity multiplier, and goal-based deficit/surplus. It clearly differentiates from sibling tools (search, analysis, alternatives) which have different purposes.

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 usage for calculating daily targets, but does not explicitly state when to use this tool versus alternatives or provide exclusions. Siblings are not related, so usage context is implied but not fully explicit.

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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