D Fetcher

Vitamin D synthesis calculator

calculate

Calculate vitamin D synthesis at a given location and time. Returns sun position, UV index, IU/min, minutes to reach the target dose, MED (sunburn threshold), the peak window, and optional 15-min day / monthly year curves. UV index uses a live satellite-driven forecast where available, with a clear-sky fallback.

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": {
      "lat": "<lat>",
      "lon": "<lon>"
    },
    "name": "calculate"
  }
}

Input Schema

Name	Required	Description	Default
`age`	No	Age in years (used for the age factor).
`day`	No	Include 15-min samples through the local day.
`lat`	Yes	Latitude in degrees.
`lon`	Yes	Longitude in degrees.
`area`	No	Exposed body fraction. 0.10 face+hands, 0.25 arms+face, 0.40 t-shirt+shorts, 0.85 near-full body.
`date`	No	ISO 8601 timestamp or epoch ms. Defaults to now.
`skin`	No	Fitzpatrick skin type 1–6. Use describe_skin_types if unknown.
`year`	No	Include monthly solar-noon samples with cloud climatology.
`scalp`	No	Scalp coverage. "bald" adds ~3.5% BSA.	hair
`target`	No	Daily vitamin D target in IU.

Schema Changelog

Changes observed during successful MCP inspections.

First observedMay 29, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It adds meaningful behavioral context by disclosing that the UV index uses a live satellite-driven forecast where available, with a clear-sky fallback. This signals data dependency and variability. It doesn't mention side effects, but as a calculator it's implicitly non-destructive, and the description covers key behavioral nuances.

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 exactly two sentences. The first sentence front-loads the purpose and enumerates key outputs, while the second explains the data source behavior. There is no fluff or repetition; every word contributes to understanding the tool.

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 the tool has 10 parameters and no output schema, the description does well to summarize the return values and the underlying forecast/fallback mechanism. It doesn't explain every parameter, but the schema covers those details. The description provides enough context for an agent to understand the tool's scope and invoke it with confidence.

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 description coverage is 100%, so the baseline is 3. The description references a few parameters indirectly (location/time, target dose, day/year curves) but doesn't add semantic meaning beyond what the schema already provides. The schema itself thoroughly documents each parameter, so the description adds marginal value here.

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 begins with a specific verb+resource: 'Calculate vitamin D synthesis at a given location and time.' It then lists concrete outputs (sun position, UV index, IU/min, etc.), making the tool's function unmistakable. This clearly distinguishes it from sibling tools describe_skin_types and geocode, which serve 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 Guidelines4/5

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

The description implies when to use the tool: given a location and time, it calculates synthesis and returns relevant metrics. It doesn't explicitly state when not to use it or name alternatives, but the sibling tools have obviously distinct roles (describing skin types, geocoding), so the context is clear enough for an agent to select appropriately.

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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