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generate_flashcards

Create educational flashcards for studying and memorization. Generate question-answer cards on any topic with customizable difficulty and quantity for effective learning.

Instructions

Generate educational flashcards for effective study and memorization.

This function creates a set of flashcards using EduChain's content engine,
focusing on key concepts, definitions, and important facts related to the topic.
Each flashcard contains a question/prompt on one side and a comprehensive
answer on the other side, optimized for spaced repetition learning.

Args:
    topic (str): The subject area, concept, or learning domain for which to
        create flashcards. Should be specific enough to generate focused content.
        Examples: "Spanish Vocabulary - Food", "Chemistry - Periodic Table",
        "History - World War I Events"
    num_cards (int, optional): The number of flashcards to generate.
        Defaults to 10. Must be between 1 and 50.
    difficulty (Optional[str]): The difficulty level for the flashcards.
        Options: "beginner", "intermediate", "advanced". If not provided,
        a mixed difficulty approach will be used.

Returns:
    Dict[str, Any]: A dictionary containing the generated flashcards and metadata.
        On success:
        - flashcards: List of flashcard objects with front and back content
        - topic: The input topic
        - count: Number of flashcards generated
        - difficulty: Difficulty level (if specified)
        On error:
        - error: Detailed error message

Raises:
    ValueError: If num_cards is not in the valid range (1-50)

Example:
    >>> generate_flashcards("Spanish Vocabulary - Animals", 5, "beginner")
    {
        "flashcards": [
            {
                "front": "What is the Spanish word for 'dog'?",
                "back": "perro (masculine noun)"
            },
            {
                "front": "Translate: 'The cat is sleeping'",
                "back": "El gato está durmiendo"
            },
            ...
        ],
        "topic": "Spanish Vocabulary - Animals",
        "count": 5,
        "difficulty": "beginner"
    }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
num_cardsNo
difficultyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it generates flashcards with question/answer pairs, uses a content engine, optimizes for spaced repetition, includes error handling with error messages, and raises ValueError for invalid num_cards. It doesn't mention rate limits, authentication needs, or destructive effects, but covers core operational behavior adequately.

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 appropriately sized and front-loaded with purpose, followed by detailed parameter and return explanations. Every sentence adds value, though the example is lengthy but informative. It could be slightly more concise by integrating some details more tightly, but overall structure is logical and efficient.

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 tool's moderate complexity (3 parameters, no annotations, but with output schema), the description is highly complete. It covers purpose, usage context, detailed parameter info, return values (including success/error cases), and provides an example. The output schema exists, so return value explanation in the description is beneficial but not strictly necessary, making this thorough.

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 fully. It excels by providing detailed parameter semantics: topic as 'subject area, concept, or learning domain' with examples, num_cards with default, range, and optional status, and difficulty with options, default behavior, and optional status. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate educational flashcards for effective study and memorization' and specifies it uses 'EduChain's content engine' with 'key concepts, definitions, and important facts.' It distinguishes from sibling tools (generate_mcqs, lesson_plan) by focusing on flashcard generation rather than multiple-choice questions or lesson plans. However, it doesn't explicitly contrast with siblings in the description text.

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

Usage Guidelines3/5

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

The description implies usage for study and memorization with spaced repetition learning, but doesn't explicitly state when to use this tool versus generate_mcqs or lesson_plan. It provides context about educational content generation but lacks specific guidance on alternative selection or exclusion criteria.

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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