Skip to main content
Glama

create_new_quiz_item

Destructive

Create a question (item) in a Canvas New Quiz, supporting choice, true-false, essay, matching, and numeric types.

Instructions

Create an item (question) in a New Quiz (LTI). Supports 5 types: choice (MCQ), true-false, essay, matching, numeric. Canvas may rate-limit rapid sequential creates. Call serially (not in parallel). For >50 items, chunk and pause between batches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYesThe Canvas course ID
assignment_idYesThe assignment ID of the New Quiz
points_possibleYesPoints awarded for a fully correct answer
positionNo1-based position in the quiz; appended if omitted
itemYes
Behavior5/5

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

Annotations already indicate destructiveHint=true and openWorldHint=true. The description adds behavioral context: 'Canvas may rate-limit rapid sequential creates' and advises serial calls and chunking. This provides valuable transparency beyond what annotations convey, with 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loaded with the core purpose, followed by key details and usage advice. Every sentence adds value; no fluff or redundancy.

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 the complexity (multiple item types, rate limits, no output schema), the description covers the essentials: purpose, types, and critical usage guidance (serial calls, chunking). It lacks return value details and prerequisites, but those are implied by the schema. It is nearly complete for an agent to use correctly.

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 80%, so many parameters already have descriptions. The description lists the 5 item types (choice, true-false, etc.) but does not add new information beyond what the schema already defines via oneOf and const values. It marginally helps by summarizing types but doesn't explain parameters in depth.

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 explicitly states 'Create an item (question) in a New Quiz (LTI).' It also lists the 5 supported types, distinguishing it from sibling tools like create_new_quiz (creates quiz) and update_new_quiz_item (updates). The verb-resource combination is clear and specific.

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?

The description provides clear usage guidelines: 'Call serially (not in parallel). For >50 items, chunk and pause between batches.' It warns about rate-limiting. While it doesn't explicitly state when not to use (e.g., use update for modifications), the context from the tool name and sibling list makes it implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bruchris/canvas-lms-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server