Burs-IA Utils

Valores atípicos (IQR)

stats-outliers-iqr

Read-onlyIdempotent

Detecta valores atípicos fuera de las vallas de Tukey (Q1 − k·IQR, Q3 + k·IQR). Cuándo: un resultado numérico tiene que ser exacto o reproducible y no estimado. No para: estandarizar cada valor como puntuación z (stats-zscore). Efectos secundarios: ninguno (solo lectura, sin red, sin estado guardado). Determinista: la misma entrada da la misma salida. Límites: entrada JSON de hasta 64 KB, 1 s por llamada, resultado de hasta 1 MB.

EN: Detects outliers outside Tukey fences Q1 − k·IQR and Q3 + k·IQR (k = 1.5 default, 3 for extreme), using linear quartiles. When: a numeric result has to be exact or reproducible instead of estimated. Not for: standardizing each value as a z-score (stats-zscore). Side effects: none (read-only, no network access, no stored state). Deterministic: the same input gives the same output. Limits: JSON input up to 64 KB, 1 s per call, result up to 1 MB.

Input Schema

Name	Required	Description	Default
`k`	No	Fence multiplier (number; optional; default 1.5; min 0; max 10).
`values`	Yes	Numbers (array of number; required; at least 4 items).

Output Schema

Name	Required	Description	Default
`q1`	No
`q3`	No
`iqr`	No
`outliers`	No
`lower_fence`	No
`upper_fence`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

addedInput schema / properties / values / examples

Added value: +[
+  [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    100
+  ]
+]

First observedNov 1, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare read-only, non-destructive, idempotent, closed-world behavior, and the description adds concrete context beyond them: determinism (same input → same output), no network, no stored state, plus hard limits (64 KB input, 1 s per call, 1 MB result). These operational bounds are exactly what an agent needs and are not derivable from the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Content is front-loaded and each ES sentence is useful, but the entire block is duplicated verbatim in English, roughly doubling length without adding information for the agent. The bilingual redundancy costs conciseness even though structure and ordering are sound.

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?

For a 2-parameter deterministic read tool with an output schema, the description covers what is computed, when to use it, the alternative, side effects, determinism, and input/output limits. Return-value details are correctly left to the output schema, so nothing needed to invoke it correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so both parameters are already documented, giving a baseline of 3. The description adds practical meaning by explaining k as a fence multiplier where 1.5 is default and 3 selects extreme outliers, which goes beyond the schema's mechanical min/max/default statements.

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?

States a specific operation with the exact formula (Tukey fences Q1 − k·IQR, Q3 + k·IQR) and the default multiplier, so the agent knows precisely what is computed. It explicitly names stats-zscore as the tool this is not, letting the agent distinguish siblings without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Provides an explicit 'Cuándo' (when a numeric result must be exact/reproducible rather than estimated) and an explicit 'No para' routing the agent to stats-zscore for z-score standardization. Both the selection condition and the exclusion are stated outright.

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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