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canvas_list_sections

List a Canvas course's sections with enrollment counts to handle section-level due dates, lab subsections, or targeted section enrollments.

Instructions

List a course's sections with their enrollment counts.

Sections matter for anything scoped to part of a roster — section-level due dates, a lab subsection, or enrolling someone into one specific section rather than the course default.

Args: course_id: numeric course id; defaults to CANVAS_DEFAULT_COURSE_ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that enrollment counts accompany each section and that the call is a listing operation, but says nothing about permission requirements (e.g., teacher/admin vs student visibility) or pagination, which matters for a roster-scoped read.

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?

Front-loads the purpose in one sentence, then a short scoping paragraph, then an Args block. Every element earns its place, though the middle paragraph is slightly verbose for a simple list tool.

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?

An output schema exists, so return values need not be described, and the single parameter plus its default environment fallback is documented. For a one-param read-only tool this is nearly complete; only permission/visibility expectations are unstated.

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 must compensate, and it does: it explains that course_id is a numeric course id and, critically, that it defaults to CANVAS_DEFAULT_COURSE_ID — a behavior invisible in the schema's empty default string. It doesn't clarify format beyond 'numeric' (e.g., leading zeros, course vs section ids).

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?

States a specific verb and resource — 'List a course's sections' — and adds that enrollment counts are returned, which is concrete. It clearly reads as distinct from course-level tools like canvas_get_course or canvas_list_enrollments, though it never names a sibling to route against.

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 middle paragraph gives real context for when sections matter: section-level due dates, lab subsections, and enrolling into a specific section 'rather than the course default.' That is genuine when-to-use guidance, though it offers no explicit exclusions or named alternative tools.

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