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Canvas LMS MCP Server

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TypeScript
Hybrid

Explain Grade

explain_grade
Read-only

Recomputes a student's weighted course grade, explaining group weights, drop rules, per-group breakdowns, letter grade, and reconciliation against Canvas's score. Use to verify or explain grades.

Instructions

Recomputes and explains the weighted course grade for a student, including assignment-group weights, drop_lowest / drop_highest / never_drop rules, per-group breakdowns (earned points, dropped assignments, weighted contributions), the mapped letter grade (via the course grading standard when present), and a reconciliation check against Canvas's posted current_score / final_score.

Use this when you need to verify that Canvas's displayed grade matches the rules, or to explain to a student or instructor how their grade was calculated.

Limitations:

  • V1 computes one student per call. Omit student_id to compute for the authenticated user.

  • Instructor-applied curves and fudge points are not exposed via the Canvas REST API and cannot be reflected in the computation; a caveat is added when the discrepancy exceeds 0.5 pp.

  • When the course uses grading periods, reconciliation is against the overall (cross-period) grade.

  • When CANVAS_PSEUDONYMIZE_STUDENTS is enabled and you are passing a student_id, first call resolve_pseudonym to obtain the real Canvas user_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYesCanvas course ID to compute the grade for.
student_idNoCanvas user_id of the student to compute the grade for. Omit to compute for the currently authenticated user. Instructors may pass any enrolled student's user_id. When CANVAS_PSEUDONYMIZE_STUDENTS is enabled, pass the numeric Canvas user_id after resolving the pseudonym via resolve_pseudonym.
assignment_group_idNoNarrow the output to a single assignment group. When omitted all groups are included and the overall course grade is computed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.18.11
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Addedv1.18.3

TDQS

A4.5/5.0
Behavior5/5

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

Even with readOnlyHint=true, the description adds substantial behavioral context: one student per call, curves/fudge points not reflected, discrepancy caveat threshold, grading-period reconciliation behavior, and pseudonym resolution requirements. This goes well beyond what the annotations provide.

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 front-loaded with the core purpose, followed by a clear use-case sentence and a tight bulleted limitations section. Every sentence contributes meaningful information, and the structure makes it easy for an agent to scan.

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, but the description enumerates what the tool returns: group weights, drop rules, per-group breakdowns, letter grade, and reconciliation check. Combined with the limitations, this is complete enough for an agent to call the tool correctly and interpret its result.

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%, so the schema already documents all three parameters. The description repeats some parameter facts (e.g., omitting student_id) but adds no new parameter-level semantics beyond what the schema states.

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 opens with a specific verb-resource pair: 'Recomputes and explains the weighted course grade for a student,' then enumerates the exact components of the computation. This detail distinguishes it from siblings like project_grade or explain_grading_policy even without naming them.

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?

It gives an explicit trigger: 'Use this when you need to verify that Canvas's displayed grade matches the rules, or to explain to a student or instructor how their grade was calculated.' It also lists important limitations, but it does not name alternatives or say when not to use this tool versus a sibling.

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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