Vitamin D synthesis calculator: sun position, UV, IU/min, time to dose, sunburn risk, daily window.
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Average 4.5/5 across 3 of 3 tools scored.
Each tool has a distinct role: calculate is the core computation, describe_skin_types provides input for skin type, and geocode converts location to coordinates. There is no overlap or ambiguity between them.
Tool names are imperative verbs in lowercase, with snake_case for multi-word names (describe_skin_types). While 'calculate' and 'geocode' are single verbs without a noun, the pattern is consistent enough and easy to predict.
Three tools is a well-scoped size for a focused vitamin D calculator server. Each tool is necessary and there are no redundant additions.
The server covers the full user journey: geocoding location, determining skin type, and calculating vitamin D synthesis with detailed outputs. No obvious dead ends or missing operations within the stated purpose.
calculateVitamin D synthesis calculatorAInspectCalculate 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.
| 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. |
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.
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.
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.
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.
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.
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.
describe_skin_typesFitzpatrick skin type referenceAInspectReturns the six Fitzpatrick skin types with sun-reactivity behavior and descriptive ancestry hints. Call this when the user's skin type is unknown so you can pick the closest match before calling calculate.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly indicates a read-only lookup ('Returns...') and specifies the output content. It does not mention error scenarios or exact return format, but for a simple reference tool this is transparent enough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary purpose, and contains no filler. Every word adds value, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter lookup tool with no output schema, the description sufficiently covers what it does, when to use it, and how it relates to a sibling. It could optionally mention that it is a static reference, but the current information is adequate for an agent to act on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (empty object). The description adds no parameter details, but none are needed. Baseline for 0 params is 4, so this is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool 'Returns the six Fitzpatrick skin types' and includes specific details about content (sun-reactivity behavior, ancestry hints). It also names the sibling tool 'calculate' and positions this tool as a prerequisite, distinguishing it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'Call this when the user's skin type is unknown' and directs the agent to use it 'before calling calculate'. This clearly states when to use and establishes a workflow with an alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
geocodeResolve a place to lat/lonAInspectTurn a place name, postal code, or "code, CC" string into { lat, lon, label }. Auto-detects US/CA/GB/NL/JP/BR postal patterns; ambiguous digit-only codes default to US unless the caller appends a country code (e.g. "10115, DE"). Falls back to Open-Meteo place-name search for everything else.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | City name, postal code, or "CODE, CC" (e.g. "75001, FR"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and excels: it discloses the auto-detection logic, the US default for ambiguous codes, the need for a country code, and the fallback to Open-Meteo search. This goes beyond a simple 'resolve a place' statement and sets clear expectations for edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, with the core purpose front-loaded and no filler. Every sentence contributes to understanding the tool's behavior and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple one-parameter tool with no output schema, the description is notably complete: it specifies the output shape ({ lat, lon, label }), covers input patterns, explains ambiguity handling, and describes the fallback behavior. There are no significant gaps for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers 100% of the parameter with a description, meeting the baseline. The tool description adds meaningful nuance by explaining the 'code, CC' format and the defaulting behavior for digit-only codes, which helps the agent format queries correctly beyond the schema's static example.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb 'Turn' and resource 'a place name, postal code, or code, CC string' into an object with lat, lon, and label. It is specific and immediately differentiates from sibling tools like calculate and describe_skin_types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on how to use the tool, including the auto-detection of postal patterns and the directive to append a country code for ambiguous digit-only codes. It does not explicitly name alternatives or when-not-to-use scenarios, but the guidance is practical and sufficient for a single-purpose geocoding tool.
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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