Burs-IA Utils

Aplanar arreglos anidados

array-flatten

Read-onlyIdempotent

Aplana arreglos anidados hasta una profundidad dada. Cuándo: hay que transformar, comparar o revisar con exactitud datos estructurados (JSON, CSV, YAML, XML, arreglos). No para: aplanar objetos y arreglos anidados en un nivel con claves con puntos (json-flatten-paths). 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: Flattens nested arrays to a given depth (default fully). When: structured data (JSON, CSV, YAML, XML, arrays) has to be transformed, compared or checked exactly. Not for: flattening nested objects/arrays into one level with dotted keys (json-flatten-paths). 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
`depth`	No	Depth (0 = no change) (integer; optional; default 100; min 0; max 100).
`items`	Yes	Array (array; required).

Output Schema

Name	Required	Description	Default
`items`	No	any JSON value

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

addedInput schema / properties / items / examples

Added value: +[
+  [
+    1,
+    [
+      2,
+      [
+        3,
+        [
+          4
+        ]
+      ]
+    ]
+  ]
+]

First observedNov 1, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare read-only, non-destructive, idempotent, and closed-world behavior. The description adds useful context beyond annotations: no network access, no stored state, deterministic output, and concrete limits (64 KB input, 1 s per call, 1 MB result).

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 front-loaded and clearly segmented into purpose, when to use, when not to use, side effects, determinism, and limits. It is longer than strictly necessary because the same information is provided in both Spanish and English, but the structure is efficient and every section is useful.

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 and the input schema is fully covered, the description provides everything else an agent needs: use cases, exclusions, side-effect profile, determinism, and operational limits. There is no significant missing context for correct invocation.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents both parameters, including depth defaults and constraints. The description adds some useful semantics such as defaulting to full flattening, but does not need to explain parameters further and largely leaves this 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 resource: flattens nested arrays to a given depth. It also clearly distinguishes itself from json-flatten-paths by naming the sibling and the exact behavior it does not implement.

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 'when' context (transforming, comparing, or checking structured data such as JSON, CSV, YAML, XML, arrays) and a 'not for' case that routes the agent to json-flatten-paths. This is actionable guidance without ambiguity.

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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