Burs-IA Utils

Puntuaciones Z

stats-zscore

Read-onlyIdempotent

Estandariza valores como (x − media) / desviación y marca los que superan un umbral. Cuándo: un resultado numérico tiene que ser exacto o reproducible y no estimado. No para: detectar valores atípicos fuera de las vallas de Tukey (stats-outliers-iqr). 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: Standardizes each value as (x − mean) / stddev (population or sample), and flags values whose |z| exceeds a threshold. When: a numeric result has to be exact or reproducible instead of estimated. Not for: detecting outliers outside Tukey fences Q1 − k·IQR and Q3 + k·IQR (stats-outliers-iqr). 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
`sample`	No	Use sample stddev (boolean; optional; default false).
`values`	Yes	Numbers (array of number; required; at least 2 items).
`threshold`	No	Outlier \|z\| threshold (number; optional; default 3; min 0).

Output Schema

Name	Required	Description	Default
`z`	No
`mean`	No
`stddev`	No
`outliers`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

addedInput schema / properties / values / examples

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First observedNov 1, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, but the description adds concrete operational context: no side effects, no network, no stored state, deterministic output, and limits of 64 KB input, 1 s per call, and 1 MB result. It also clarifies population-vs-sample behavior, which has practical consequences for the computation.

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 content is well organized with explicit labels for when, not-for, side effects, determinism, and limits, and the core formula is front-loaded. The only conciseness cost is the full bilingual duplication, which doubles the text even though the English and Spanish sections carry the same information.

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?

Given that an output schema exists, the description need not explain return values, and it already covers limits, side effects, determinism, and alternative routing. With complete schema coverage and rich annotations, the definition gives the agent everything needed to select and invoke the tool correctly.

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 description coverage is 100%, so the schema already documents values, sample, and threshold. The description still adds useful semantic detail: it gives the standardization formula, mentions population or sample stddev, and explains that threshold flags values whose |z| exceeds it, which clarifies the threshold direction beyond the schema text.

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 gives a specific verb and formula: standardizes values as (x − mean) / stddev and flags values whose |z| exceeds a threshold. It explicitly distinguishes the tool from stats-outliers-iqr, so an agent can identify the correct tool without opening the 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?

It states exactly when to use it (when a numeric result must be exact or reproducible rather than estimated) and explicitly says when not to use it, naming stats-outliers-iqr as the alternative for Tukey-fence outliers. This is a clear routing instruction with an explicit exclusion.

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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