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Burs-IA Utils

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Convertir a Tipo Título

case-title
Read-onlyIdempotent

Convierte a formato de título; con lang en/es mantiene en minúscula palabras cortas (de, la, y…). Cuándo: hay que transformar, comparar o medir un texto con exactitud y no por aproximación. No para: detectar la convención de nombre de un identificador (case-detect). 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: Capitalizes words as a title. With lang en or es, short function words (of, the / de, la, y…) stay lowercase except at the start; lang none capitalizes every word. When: text has to be transformed, compared or measured exactly instead of approximated. Not for: detecting the naming convention of an identifier (case-detect). 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

NameRequiredDescriptionDefault
langNoSmall-word rules (optional; one of: en, es, none; default "en").en
textYesText (string; required; up to 4096 chars).

Output Schema

NameRequiredDescriptionDefault
resultNoany JSON value

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / text / examples
      Added value: +[
+  "the lord of the rings"
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, and the description partly restates those ('read-only, no network access, no stored state'). It earns credit for adding non-annotation facts: determinism, a 1 s per-call budget, and 64 KB input / 1 MB result ceilings.

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?

The content is front-loaded and each section (When / Not for / Side effects / Deterministic / Limits) is clearly labeled, but the full text is duplicated in Spanish and English, roughly doubling the token cost for an agent that only needs one language.

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?

An output schema exists, so return values need no explanation. The description covers the selection criteria, the alternative tool, side effects, determinism, and resource limits, leaving nothing an agent needs in order 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 baseline is 3, but the description goes beyond the enum labels by explaining what each lang value does: en/es keep short function words lowercase except at the start, while none capitalizes every word. That is genuine semantic meaning the schema does not convey.

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 ('Capitalizes words as a title') and immediately qualifies the scope with the lang-dependent rule. It also names the sibling it is not (case-detect), so an agent can separate it from the case-* family without opening schemas.

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 'When' clause (transforming/comparing/measuring text exactly) and an explicit 'Not for' clause routing identifier-convention detection to case-detect. Both the positive trigger and the exclusion are stated, not inferred.

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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