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lesson_plan

Create structured lesson plans with learning objectives, activities, and assessments for any educational topic and grade level.

Instructions

Generate a comprehensive, structured lesson plan for a given educational topic.

This function creates a detailed lesson plan using EduChain's content engine,
including learning objectives, materials needed, activities, and assessment methods.
The lesson plan follows educational best practices and can be customized for
different grade levels and durations.

Args:
    topic (str): The subject, concept, or learning objective for the lesson.
        Should be specific and focused. Examples: "Introduction to Fractions",
        "The American Revolution", "Basic HTML Tags"
    duration (Optional[str]): The intended duration of the lesson.
        Examples: "45 minutes", "1 hour", "2 class periods". If not provided,
        a standard duration will be assumed.
    grade_level (Optional[str]): The target grade level or educational level.
        Examples: "Grade 5", "High School", "College Level", "Adult Education".
        If not provided, a general approach will be used.

Returns:
    Dict[str, Any]: A comprehensive lesson plan dictionary containing:
        On success:
        - title: Lesson title
        - objectives: Learning objectives and goals
        - materials: Required materials and resources
        - activities: Structured learning activities
        - assessment: Methods for evaluating student learning
        - duration: Lesson duration
        - grade_level: Target grade level
        On error:
        - error: Detailed error message

Example:
    >>> lesson_plan("Photosynthesis", "50 minutes", "Grade 7")
    {
        "title": "Understanding Photosynthesis",
        "objectives": ["Students will understand...", "Students will be able to..."],
        "materials": ["Textbook", "Microscope", "Plant samples"],
        "activities": [
            {
                "name": "Introduction",
                "duration": "10 minutes",
                "description": "..."
            },
            ...
        ],
        "assessment": "Quiz on key concepts",
        "duration": "50 minutes",
        "grade_level": "Grade 7"
    }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
durationNo
grade_levelNo

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 the full burden of behavioral disclosure. It effectively describes the tool's behavior: it uses 'EduChain's content engine,' follows 'educational best practices,' and can be 'customized for different grade levels and durations.' It also details the return structure and error handling. However, it does not mention potential limitations like rate limits, authentication needs, or content generation constraints.

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 and appropriately sized, starting with a clear purpose statement, followed by functional details, parameter explanations, return values, and an example. Most sentences earn their place by adding value, though some parts could be slightly more concise (e.g., the example is detailed but necessary). It is front-loaded with key information.

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 complexity (generating educational content), no annotations, and an output schema provided, the description is complete. It explains the tool's purpose, behavior, parameters, return structure (including success and error cases), and includes a practical example. The output schema existence means the description doesn't need to detail return values further, and it adequately covers all necessary contextual aspects.

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?

The schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all three parameters: 'topic' with examples and specificity guidance, 'duration' with examples and default behavior, and 'grade_level' with examples and default behavior. This adds significant meaning beyond the basic schema, ensuring clarity on usage and expectations.

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 states the tool's purpose: 'Generate a comprehensive, structured lesson plan for a given educational topic.' It specifies the verb ('generate'), resource ('lesson plan'), and scope ('comprehensive, structured'), distinguishing it from sibling tools like 'generate_flashcards' and 'generate_mcqs' which focus on different educational resources.

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 creating lesson plans with educational best practices and customization for grade levels/durations, but does not explicitly state when to use this tool versus alternatives like the sibling tools. It mentions customization features but lacks explicit guidance on scenarios where this tool is preferred over others or any exclusions.

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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