Skip to main content
Glama

Convertir a kebab-case

case-kebab
Read-onlyIdempotent

Convierte texto o un identificador a kebab-case, separando palabras por espacios, signos y mayúsculas. 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: Converts text or an identifier to kebab-case, splitting words on spaces, punctuation and camelCase/acronym boundaries. Unicode letters are preserved. 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
textYesText or identifier (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: +[
      +  "helloWorld Foo_bar"
      +]
  2. First observed

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 earns credit on top by adding determinism ('same input gives the same output'), the absence of network/state, and concrete limits (64 KB input, 1 s per call, 1 MB result). Those limits are behavioral facts an agent cannot infer from annotations.

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?

Front-loaded with the core action, then labeled sections (When / Not for / Side effects / Deterministic / Limits), so an agent can scan selectively. The Spanish and English blocks are fully parallel, roughly doubling length; useful for bilingual serving but not maximally economical.

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 single-parameter pure transform with an output schema, every decision-relevant fact is present: input limits, timing, output size cap, determinism, no side effects, and the sibling exclusion. Nothing an agent needs before calling is missing.

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 single 'text' parameter is already documented; baseline would be 3. The description adds real meaning beyond the schema by explaining how words are split and that Unicode letters survive, which affects what inputs are valid and what output to expect, though it gives no concrete example beyond the schema's own.

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 (convert text/identifier to kebab-case) and even specifies the splitting rules: spaces, punctuation, camelCase/acronym boundaries, plus Unicode letter preservation. It explicitly names the sibling it is not (case-detect), so an agent can distinguish it from the ~10 other case-* tools 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?

Uses explicit 'When:' and 'Not for:' framing, naming the alternative tool (case-detect) and the condition that selects it (detecting a naming convention versus transforming). This is the exact pattern that routes an agent correctly among close siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources