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create_quiz

Create a quiz with configurable questions, passing percentage, maximum attempts, and answer display settings. Set duration, shuffle order, and negative marking rules.

Instructions

Create an LMS Quiz with optional questions.

Args: title: Quiz title (slug auto-generated). passing_percentage: Minimum percentage to pass (0-100). questions: JSON list of [{"question":"","marks":5}]. max_attempts: Max attempts (0 = unlimited). show_answers: Show correct answers after submission. duration: Time limit in minutes (as string). shuffle_questions: Randomise question order. enable_negative_marking: Deduct marks for wrong answers. marks_to_cut: Marks to deduct per wrong answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
durationNo
questionsNo
marks_to_cutNo
max_attemptsNo
show_answersNo
shuffle_questionsNo
passing_percentageNo
enable_negative_markingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It effectively explains that a quiz is created, slug is auto-generated, and parameters control behavior (e.g., passing_percentage, max_attempts). However, it does not mention side effects like immediate persistence or if any notification is sent.

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 description is concise: a one-line summary followed by a clear bullet list of parameters. Every sentence adds value, and there is no redundant text.

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?

Given 9 parameters, 1 required, and the existence of an output schema, the description covers all parameters adequately. It does not discuss return values, but the output schema likely covers that. It could mention prerequisites or system interactions for full completeness.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates well. Each parameter has a brief but meaningful explanation (e.g., 'slug auto-generated' for title, 'JSON list of...' for questions). Some details could be sharper (e.g., 'duration' format as string), but overall it clarifies usage.

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 'Create an LMS Quiz with optional questions.' It identifies the verb (create) and resource (quiz). While it distinguishes from sibling quiz tools like 'get_quiz' or 'add_question_to_quiz', it does not explicitly differentiate from potential alternative creation methods.

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 a quiz with optional questions, but lacks explicit guidance on when to use this tool versus alternatives (e.g., adding questions later via 'add_question_to_quiz'). No when-not-to or prerequisite context is provided.

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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