SkoIA Parts

Calculate fit guidance

get_fit_guidance

Read-onlyIdempotent

Calculates deterministic manufacturing compensation and returns an explicit physical-test warning.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`gender`	No
`fitType`	Yes
`process`	No
`material`	No
`nominalMm`	Yes
`measurementBasis`	No
`userCalibrationMm`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`tool`	Yes
`facts`	Yes
`status`	Yes
`warnings`	Yes
`requestId`	Yes
`confidence`	Yes
`provenance`	Yes
`schemaVersion`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 2, 2026
- addedOutput schema / properties / confidence / properties / score / anyOf
```
Added value: +[
+  {
+    "maximum": 1,
+    "minimum": 0,
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / confidence / properties / score / maximum
```
Removed value: -1
```
- removedOutput schema / properties / confidence / properties / score / minimum
```
Removed value: -0
```
- removedOutput schema / properties / confidence / properties / score / type
```
Removed value: -"number"
```
First observedSep 1, 2026

TDQS

B3.2/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds a valuable behavioral disclosure: the result includes an explicit physical-test warning, and 'deterministic' reinforces the idempotent semantics. No contradiction with annotations.

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 single sentence is tight and front-loaded, with no filler words. It is not bloated, but it may be too terse to be fully 'appropriately sized' for a seven-parameter tool; still, the conciseness itself is strong.

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

Completeness2/5

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

The output schema reduces the need to describe return values, but the input side is under-specified: seven parameters with no schema descriptions and no parameter mentions in the description. An agent cannot determine the semantics of gender, measurementBasis, or userCalibrationMm, nor is the tool clearly positioned against its siblings.

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

Parameters1/5

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

Schema description coverage is 0% and the description mentions none of the seven parameters, so it adds no meaning beyond the raw schema. It does not explain nominalMm, fitType, measurementBasis, userCalibrationMm, or the free-text process/material fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action (calculates), a specific object (deterministic manufacturing compensation), and a distinctive output behavior (explicit physical-test warning). It is not a tautology and is distinguishable from siblings like get_compatibility, though it does not explicitly compare with them.

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?

No explicit when/when-not/alternative guidance is given. The phrase 'deterministic manufacturing compensation' implies the use case, but an agent gets no direction on when to prefer this over get_compatibility or other sibling tools, and no exclusions are stated.

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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