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canvas_list_students

List students enrolled in a Canvas course, with optional email inclusion, name/login search filtering, and FERPA-compliant PII redaction via hashes.

Instructions

List the students enrolled in a course.

FERPA: returns student names, and email addresses when include_email=True. With CANVAS_REDACT_PII=1 identifiers come back as stable hashes instead.

Args: course_id: numeric course id; defaults to CANVAS_DEFAULT_COURSE_ID. include_email: also request email addresses (default False). search_term: optional name/login substring filter (3+ characters).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idNo
search_termNo
include_emailNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden and does so well: it discloses FERPA-relevant output (names, conditional emails) and the CANVAS_REDACT_PII=1 hashing behavior, which an agent cannot derive from the schema. It omits pagination behavior and permission requirements, keeping it short of a 5.

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-loaded with the purpose, then a compact FERPA note, then an Args block that maps one line per parameter. No filler sentences; the formatting is slightly rough but efficient.

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 description covers the non-obvious behavior (PII redaction, defaults, filter constraints). Missing pagination and result-limit context is the only real gap.

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 description coverage is 0%, so the description must compensate, and it does: it documents all three parameters, including the CANVAS_DEFAULT_COURSE_ID fallback and the 3-character minimum on search_term. It stops short of clarifying that course_id is a string field.

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+resource: 'List the students enrolled in a course.' An agent immediately knows the operation and scope. However, it does not distinguish itself from the nearby sibling canvas_list_enrollments, which plausibly overlaps in purpose.

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

Usage Guidelines2/5

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

The description says nothing about when to use this tool versus alternatives such as canvas_list_enrollments or canvas_get_gradebook, nor any prerequisites beyond the default course id. Usage must be inferred from the name alone.

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