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Subsecuencia común más larga

longest-common-subsequence
Read-onlyIdempotent

Encuentra la subsecuencia común más larga de dos cadenas. Cuándo: hay que transformar, comparar o medir un texto con exactitud y no por aproximación. No para: calcular las diferencias línea por línea entre dos textos (diff-lines). 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: Finds the longest common subsequence of two strings (by code point) and its length. Length product capped at 4M. When: text has to be transformed, compared or measured exactly instead of approximated. Not for: computing a line-by-line diff of two texts (diff-lines). 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
aYesFirst (string; required; up to 20000 chars).
bYesSecond (string; required; up to 20000 chars).

Output Schema

NameRequiredDescriptionDefault
lengthNo
subsequenceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / a / examples
      Added value: +[
      +  "ABCBDAB"
      +]
    • addedInput schema / properties / b / examples
      Added value: +[
      +  "BDCABA"
      +]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (read-only, deterministic), it adds crucial operational context regarding side effects (no network, no state), and hard limits (64KB input, 1s latency).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is highly structured, using clear headings (When, Not for, Side effects, Limits) to organize complex behavioral information efficiently.

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 deterministic, side-effect-free, calculation-based tool, the description fully covers all necessary contexts, limits, and safety assurances.

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?

Since schema description coverage is 100%, the description provides no unique semantic value regarding the parameters 'a' or 'b'; baseline score is appropriate.

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?

The description clearly defines the core mathematical function (finding the LCS) and provides a clear distinction from related text manipulation tools like line-by-line diff.

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?

It gives explicit 'when' and 'not for' scenarios, specifically directing users away from approximation and towards exact comparison, and referencing the 'diff-lines' sibling tool.

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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