SooTool

geometry.vector_norm

Read-onlyIdempotent

Compute a vector's L-p norm from decimal strings for p ≥ 1, defaulting to Euclidean length, and return a high-precision result.

Instructions

벡터의 L-p 노름 (Σ |v_i|^p)^(1/p) 을 계산한다. v 는 비어 있지 않은 Decimal 문자열 리스트, p 는 1 이상의 정수(기본 2, 유클리드 길이). mpmath 50자리 계산 후 유효숫자 30자리로 반환한다. 최댓값 노름(p=∞)과 정수가 아닌 p 는 지원하지 않는다.

Input Schema

Name	Required	Description	Default
`p`	No
`v`	Yes

Output Schema

Name	Required	Description
`_meta`	No	서버가 붙이는 영수증(integrity), 엔진, 힌트
`trace`	Yes	계산 근거(tool, formula, inputs, steps, output)
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 29, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, closed-world), so the description correctly concentrates on what annotations cannot convey: internal precision (mpmath at 50 digits) and output rounding (30 significant digits), plus the accepted input domain. This is genuinely useful pre-call knowledge about numerical fidelity that would otherwise be invisible.

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?

Three tight sentences, front-loaded with the operation and formula, then input contract, then numerical behavior, then exclusions. Every clause adds information an agent needs; nothing is padding.

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

Completeness5/5

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

With only two parameters, a 0% schema-coverage gap, and an output schema that already documents the return shape, the description covers everything the agent needs: input types, the meaning and default of p, precision behavior, and unsupported cases. Nothing material is left to guess.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries the entire burden, and it does: it specifies that v is a non-empty array of Decimal strings (not floats), and that p is an integer ≥ 1 with default 2 meaning Euclidean length. It even clarifies the limits on p, which the schema's bare `integer` type does not.

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?

Names a specific verb and resource ('벡터의 L-p 노름') and even spells out the formula (Σ|v_i|^p)^(1/p), which instantly separates it from siblings like geometry.vector_dot and geometry.vector_cross. No other sibling computes a norm, so the boundary is unambiguous.

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 gives explicit applicability constraints: v must be a non-empty list of Decimal strings, p is an integer ≥ 1, default is 2. It also states exclusions (p = ∞ max norm and non-integer p are unsupported), which prevents misuse. It stops short of naming a fallback tool for those unsupported cases, so it is clear context without full alternative routing.

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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