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N-gramas

ngrams
Read-onlyIdempotent

Genera n-gramas de caracteres o palabras con sus conteos. Cuándo: hay que transformar, comparar o medir un texto con exactitud y no por aproximación. No para: contar la frecuencia de palabras en un texto (word-frequency). 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: Produces character or word n-grams (n = 1-10) with counts, sorted by frequency. Useful for keyword or similarity features. When: text has to be transformed, compared or measured exactly instead of approximated. Not for: counting word frequencies in text (word-frequency). 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
nNon (integer; optional; default 2; min 1; max 10).
topNoTop N (integer; optional; default 20; min 1; max 1000).
textYesText (string; required).
unitNoUnit (optional; one of: word, char; default "word").word

Output Schema

NameRequiredDescriptionDefault
totalNo
ngramsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / text / examples
      Added value: +[
      +  "to be or not to be"
      +]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, and the description restates that ("solo lectura, sin red, sin estado guardado") rather than adding to it. What it does add beyond annotations is genuinely useful: determinism guarantee and concrete limits (64 KB JSON input, 1 s per call, 1 MB result), which an agent cannot infer from structured fields. Some duplication of the annotation profile 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 with labeled sections (Cuándo / No para / Efectos / Límites), but the entire Spanish and English blocks are content-identical, roughly doubling length without adding information. Every sentence earns its place individually, but the bilingual duplication does not.

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 prose. Usage routing, side effects, determinism and hard limits are all covered for a 4-parameter read-only text tool. Nothing an agent needs in order to call it correctly is missing.

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 description coverage is 100%, so n, top, text and unit are fully documented with types, defaults, ranges and an enum. The description only echoes the n=1-10 range and the word/char choice, and never mentions the top parameter or its truncation behavior. Baseline 3 is appropriate when the schema carries the parameter burden.

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+resource ("Produces character or word n-grams (n = 1-10) with counts, sorted by frequency") and immediately distinguishes the output shape by sorting order. The nearby sibling word-frequency is named as the thing it is not, so an agent can route without opening either 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?

Has an explicit "When" clause (exact transform/compare/measure vs. approximation) and an explicit "No para / Not for" clause that names the alternative tool, word-frequency, by name. This is the strongest possible usage routing short of listing prerequisites.

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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