On the Register

energy_tdee

Daily energy use (BMR via Mifflin-St Jeor, then TDEE for activity) and two safe calorie-deficit targets for weight loss, clamped to the NHS lower limits. Needs age and sex for the equation. General information, not a diet plan.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "weight_kg": "<weight_kg>",
      "height_cm": "<height_cm>",
      "age": "<age>",
      "sex": "<sex>"
    },
    "name": "energy_tdee"
  }
}

Input Schema

Name	Required	Description
`age`	Yes	Age in years (18-100)
`sex`	Yes	Biological sex, for the metabolic equation
`activity`	No
`height_cm`	Yes	Height in cm (120-230)
`weight_kg`	Yes	Body weight in kg (30-350)

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the full load and does reasonably well: it discloses the underlying equation, the safety clamping to NHS lower limits, and a scope disclaimer. Gaps remain on behavior for the optional 'activity' parameter (what happens when omitted) and on the units/format of the returned targets.

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?

Two tight sentences with the computation and outputs front-loaded and the safety caveat last. It is dense but not bloated; only the phrase 'Needs age and sex for the equation' borders on redundant given the schema.

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?

For a pure calculation tool with no output schema, the description conveys the method, the two result targets, and the safety clamp, which is most of what an agent needs. It falls short only on output units and default-activity behavior, which are minor for a deterministic calculator.

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 80%, so the schema already explains age, sex, height, and weight; the description only reinforces the age/sex requirement. The 'activity' enum is the one undocumented parameter, and the description mentions activity drives TDEE but does not explain the five levels or the default, leaving that gap partly open.

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 names a specific computation and its outputs: BMR via Mifflin-St Jeor, TDEE by activity, plus two deficit targets clamped to NHS limits. That is concrete enough to separate it from siblings like bmi_check, protein_target, or weight_ideal, which compute different quantities.

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?

It states prerequisites ('Needs age and sex for the equation') and sets a boundary ('General information, not a diet plan'), which implies the appropriate context. But it never says when to prefer this over bmi_check or weight_ideal, nor does it exclude any use case explicitly, so routing still requires inference.

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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