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Create New Quiz

create_new_quiz
Destructive

Create a New Quiz (LTI) in a Canvas course, specifying title, due dates, publish status, and instructions.

Instructions

Create a New Quiz (LTI) in a Canvas course. New Quizzes is the modern quiz engine; for Classic quizzes use create_quiz.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesTitle of the quiz
due_atNoISO-8601 due date
lock_atNoISO-8601 lock time
course_idYesThe Canvas course ID
publishedNoWhether the quiz is visible to students
unlock_atNoISO-8601 unlock time
instructionsNoHTML instructions shown before the quiz starts
points_possibleNoTotal points; defaults to sum of item points

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv1.18.8
    • removedInput schema / properties / instructions / anyOf
      Removed value: -[
      -  {
      -    "type": "string"
      -  },
      -  {
      -    "type": "null"
      -  }
      -]
    • addedInput schema / properties / instructions / type
      Added value: +[
      +  "string",
      +  "null"
      +]
  2. First observedv1.18.0

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already signal destructiveHint, and the description confirms this is a create/mutating operation, so there is no contradiction. However, the description adds only the 'LTI' and 'modern quiz engine' context, not deeper behavioral details like permissions, draft/published defaults, or whether existing data is affected. With annotations present, this is acceptable but not richly 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 no filler: the first states the action and scope, the second provides the critical sibling distinction. The information is front-loaded and every sentence 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 create tool with a fully described schema and safety-relevant annotations, the description is mostly complete. It explains what is created and how it differs from Classic quizzes. It does not mention return values or permissions, but the schema and annotations cover most invocation needs.

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 schema already documents all eight parameters, including required fields and semantics. The description itself adds no parameter-level detail, so the baseline score of 3 is appropriate.

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: 'Create a New Quiz (LTI) in a Canvas course.' It also distinguishes this tool from Classic quizzes by naming create_quiz, making it easy for an agent to select the correct tool even among many siblings.

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

Usage Guidelines5/5

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

The description explicitly says 'New Quizzes is the modern quiz engine; for Classic quizzes use create_quiz.' This gives a clear when-to-use and identifies the alternative, leaving little to inference.

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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