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Procesar carpeta de imágenes

procesar_carpeta

Process all images in a folder by transcribing each with local OCR and generating an output file per image, avoiding token usage for large batches.

Instructions

Procesa TODAS las imágenes de un directorio: transcribe cada una con OCR local y genera un archivo de salida por imagen, sin gastar tokens. Ideal para lotes grandes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idiomaNoCódigo de idioma del texto (por defecto "eng").
carpetaYesDirectorio con las imágenes a procesar (dentro de la raíz permitida).
formatoNoFormato de los archivos de salida (por defecto "md").
carpetaSalidaNoDirectorio donde guardar los resultados. Por defecto "carpeta".
conDescripcionNoGenerar descripción breve por imagen (IA si hay API key, si no, local).
incluirBloquesNoIncluir la transcripción por bloques en cada archivo.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and performs well: it reveals that OCR is local ('OCR local'), that no tokens are spent ('sin gastar tokens'), and that the tool writes one output file per image ('genera un archivo de salida por imagen'). It does not cover edge behaviors like subdirectory recursion, overwriting existing files, or handling of non-image files, but the core behavioral profile an agent needs is transparent.

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?

Two sentences with zero waste: the first front-loads the core behavior (scope, mechanism, output granularity, cost) and the second adds the intended use case. Every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter batch tool with no output schema, the description conveys the essential contract: input is a folder, output is one file per image with transcription. It does not explain output-location defaults (carpetaSalida defaulting to carpeta) or subdirectory behavior, but the core workflow an agent needs to decide and invoke correctly is present.

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 the input schema already documents all six parameters (carpeta, idioma, formato, carpetaSalida, conDescripcion, incluirBloques) with descriptions. The description adds no per-parameter detail beyond the schema, so the baseline 3 applies.

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 states a specific verb and resource: it processes ALL images in a directory ('Procesa TODAS las imágenes de un directorio'), then specifies the action (local OCR transcription, one output file per image). The capitalized 'TODAS' and the 'Ideal para lotes grandes' note sharply differentiate it from the sibling transcribir_imagen, which is the single-image counterpart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: use this for large batches ('Ideal para lotes grandes') and when conserving tokens matters ('sin gastar tokens'). It implies the selection criterion versus the siblings (batch vs. single image, local vs. token-consuming) but never explicitly names an alternative or states a when-not-to-use condition, so it stops short of a 5.

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