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Canvas LMS MCP – Remote Edition

Canvas LMS MCP – Remote Edition

Assignment Brief

assignment_brief
Read-onlyIdempotent

Retrieve everything needed to start a Canvas assignment in one call: instructions, due and lock dates, submission method, attempts, rubric, late policy, files, group and peer-review details, and your status.

Instructions

Everything needed to start an assignment, in one call: the instructions as clean text, due and lock dates, how to submit, attempts, the rubric, the late policy, linked files, group and peer-review details, and your current status.

Input Schema

NameRequiredDescriptionDefault
course_idYesCanvas course ID, a numeric string such as "12345". Get it from list_courses.
assignment_idYesAssignment ID (numeric string). Get it from list_assignments or list_upcoming_assignments.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.3.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. Beyond that, the description usefully discloses the breadth of the composite response, including derived/processed content ('instructions as clean text') and personalized content ('your current status'), which is real value not present in 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?

A single sentence with the value proposition front-loaded ('Everything needed to start an assignment, in one call') followed by a compact enumeration of returned fields. The long list is not padding – it is the core payload information for a tool with no output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must carry the return-value burden, and it does so thoroughly by listing the response contents. With 100% parameter documentation and full annotation coverage, nothing an agent needs to invoke this read-only aggregate correctly is missing.

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% and both parameters already document their format and how to obtain them from list_courses and list_assignments. The description adds no additional meaning about the two inputs, so the baseline 3 applies.

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?

Specific verb+resource: it states it returns everything needed to start an assignment in one call, and enumerates the returned contents (instructions, dates, submission method, attempts, rubric, late policy, files, group/peer-review, status). The phrase 'in one call' implicitly distinguishes it from piecemeal siblings like get_assignment or get_late_policy, but none are named explicitly, so it stops short of a 5.

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 phrase 'to start an assignment' implies the moment of use, but there is no explicit when-to-use statement, no when-not, and no named alternative (e.g., 'use get_assignment for a lightweight metadata-only fetch'). Usage is inferred rather than stated.

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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