Burs-IA Utils

Estadística descriptiva

stats-describe

Read-onlyIdempotent

Calcula la estadística descriptiva: media, mediana, moda, mínimo, máximo, varianza y desviación estándar, cuartiles. Cuándo: un resultado numérico tiene que ser exacto o reproducible y no estimado. No para: calcular percentiles (stats-percentile). 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: Computes the count, sum, mean, median, mode(s), min, max, range, population and sample variance/standard deviation, and quartiles (linear method) of a list of numbers. When: a numeric result has to be exact or reproducible instead of estimated. Not for: computing one or more percentiles (stats-percentile). 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
`values`	Yes	Numbers (array of number; required; at least 1 items).

Output Schema

Name	Required	Description	Default
`q1`	No
`q3`	No
`max`	No
`min`	No
`sum`	No
`mean`	No
`mode`	No
`count`	No
`range`	No
`median`	No
`stddev_sample`	No
`variance_sample`	No
`stddev_population`	No
`variance_population`	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?

Beyond the simple hints provided in annotations, the description adds critical operational details, including low-level constraints like 'no network access,' 'no stored state,' and precise performance limits (64 KB input, 1 s call, 1 MB output).

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 description is highly dense, balancing multiple complex features (functionality, limits, behavioral traits) into a single block. It is efficient but could be slightly more segmented for faster AI parsing.

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?

The tool is exceptionally complete, covering functional scope, operational limits, safety profiles (read-only/deterministic), and the exact boundaries of its applicability relative to sibling tools.

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?

While schema coverage is 100%, the description adds critical semantic context regarding the input, specifying it must be a JSON structure containing a list of numbers for calculation, rather than just a general list of numbers.

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 specifies the exact calculations performed (mean, median, mode, etc.). Crucially, it distinguishes itself by explicitly stating what it is NOT, referencing the sibling tool stats-percentile.

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?

Usage is tightly governed by constraints: 'When' refers to needing exact/reproducible results, and 'Not for' immediately redirects the user to the correct alternative, stats-percentile.

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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