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Eduvent

Anki Card Manager (acm)

by Eduvent

acm_annotate

Annotate candidate flashcards by detecting duplicates across decks, suggesting optimal deck and tags, and assessing confidence levels—all without uploading or persisting any data.

Instructions

Anota tarjetas candidatas SIN subir ni persistir nada (E3-1 / RF-A2).

Es el corazón del flujo "crear → revisar → subir": Claude propone cards y llama a esto; cada card vuelve anotada para que muestres al usuario una lista YA deduplicada y clasificada. No escribe en el registro ni en Anki.

Por cada card devuelve:

  • es_duplicado (bool) + matches (id, deck, score, razón) cross-deck

  • mazo_sugerido, tags_sugeridos (alta confianza), tags_ambiguos

  • confianza (high/medium/low), flags_calidad, material_origen

Args: cards_json: JSON array. Cada card: front, back, source, y opcionales suggested_tags ("category::value"), note_type, profile, deck, material_origen (PDF/sección de origen). verbose: False (default) → matches compactos (top-2) para economía de tokens (§9); True → detalle completo de cada match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
verboseNo
cards_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so the description fully carries the burden. It explicitly states the tool is non-persistent (read-only), details the return structure (es_duplicado, matches, mazo_sugerido, etc.), and explains verbosity behavior. No contradictions.

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?

The description is well-structured with a clear first sentence, followed by behavioral and parameter details. While comprehensive, it could be slightly more concise (e.g., integrating the return details more tersely), but overall it is efficient and organic.

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?

Given the presence of an output schema (context signal), the description need not explain return values, but it does so anyway. It covers all necessary aspects: purpose, behavior, parameters, and usage context. Sibling tools are listed, providing additional orientation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It thoroughly explains both parameters: cards_json as a JSON array with required and optional fields, and verbose as a boolean controlling match detail. This adds significant value beyond the schema.

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 ('anota') and resource ('tarjetas candidatas'), clearly distinguishing it from siblings by emphasizing it does not persist data (SIN subir ni persistir nada). This directly addresses the core workflow step.

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?

Description clearly situates the tool in the 'crear → revisar → subir' workflow and states it does not write to registry or Anki, implying when to use. However, no explicit when-not or alternative sibling comparisons are given, leaving room for minor ambiguity.

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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