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canvas-student-mcp

canvas-student-mcp

by xmike04
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

List modules

canvas_list_modules
Read-only

Retrieve a course's modules and their items in order to view the content outline, including pages, files, assignments, quizzes, and links.

Instructions

List a course's modules with their items (pages, files, assignments, quizzes, links) in order — the course's content outline.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "course_id": "<course_id>"
    },
    "name": "canvas_list_modules"
  }
}

Input Schema

NameRequiredDescriptionDefault
course_idYesCanvas course ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds value beyond these by specifying that the output includes items in order and represents the content outline. This provides useful behavioral context without contradicting the annotations.

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 a single, well-structured sentence that is front-loaded with the core action and resource. Every word adds value, with no redundancy or filler. It is concise yet informative.

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 absence of an output schema, the description reasonably explains the return value: a list of modules with items, in order, representing the course outline. This is sufficient for an open-world read tool, though it could briefly mention the structure of module objects. Overall, it is complete enough for the tool's complexity.

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?

The input schema has 100% description coverage for the single parameter 'course_id', which is adequately described as 'Canvas course ID'. The tool description does not add any additional semantics or usage notes for the parameter, 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 clearly states the verb 'List', the resource 'a course's modules', and specifies what is included ('their items (pages, files, assignments, quizzes, links) in order'). It also provides a concise summary ('the course's content outline'), which distinguishes it from sibling tools like canvas_list_assignments or canvas_list_courses.

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 the tool is for retrieving the course content outline, but it does not explicitly state when to use this tool versus alternatives (e.g., canvas_get_module_progress for progress, or canvas_list_assignments for assignments). No when-not or exclusion criteria are provided, leaving the agent to infer usage context.

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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