Burs-IA Utils

Media móvil

stats-moving-average

Read-onlyIdempotent

Calcula la media móvil simple (SMA) o exponencial (EMA) de una serie. Cuándo: un resultado numérico tiene que ser exacto o reproducible y no estimado. No para: pronosticar una serie numérica con suavizado exponencial simple, Holt (stats-forecast). 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 simple (SMA) or exponential (EMA, alpha = 2/(window+1)) moving average of a series; SMA returns null until the window fills. When: a numeric result has to be exact or reproducible instead of estimated. Not for: forecasting a numeric series with simple exponential smoothing, Holt (stats-forecast). 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
`type`	No	Type (optional; one of: sma, ema; default "sma").	sma
`values`	Yes	Numbers (array of number; required; at least 1 items).
`window`	Yes	Window size (integer; required; min 1; max 10000).

Output Schema

Name	Required	Description	Default
`values`	No	any JSON value

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 2, 2026
- addedInput schema / properties / values / examples
```
Added value: +[
+  [
+    1,
+    2,
+    3,
+    4,
+    5
+  ]
+]
```
- addedInput schema / properties / window / examples
```
Added value: +[
+  3
+]
```
First observedNov 1, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Adds real behavior beyond the annotations: SMA returns null until the window fills, EMA uses alpha = 2/(window+1), and operational limits (64 KB input, 1 s per call, 1 MB result) are disclosed. Some sentences restate what annotations already declare (read-only/no side effects, determinism vs. idempotentHint=true), which keeps it from a 5.

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?

Well front-loaded (purpose, then when/not-for, then effects, then limits), but the entire content is duplicated verbatim in Spanish and English, doubling length without adding information. Every sentence earns its place individually, but the payload is delivered twice.

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 pure computation with an output schema and full annotation coverage, the description covers purpose, routing, edge-case output (leading nulls), limits, and determinism. An agent has everything needed to call it 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 coverage is 100%, so the baseline is 3; the description earns more by supplying the EMA window-to-alpha formula and the sma default, meaning the schema does not express. The `values` and `window` semantics are otherwise left to the schema.

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 verb and named resource (computes SMA or EMA of a series) and immediately names the sibling it is not (stats-forecast). An agent can distinguish it from stats-forecast, stats-means and stats-describe without opening any 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?

Explicitly gives the selection condition ("a numeric result has to be exact or reproducible instead of estimated") and the exclusion with the alternative tool named: not for forecasting with SES/Holt, use stats-forecast. Nothing is left to inference.

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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