Canvas LMS MCP Server

Explain Grade

explain_grade

Read-only

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

Name	Required	Description
`course_id`	Yes	Canvas course ID to compute the grade for.
`student_id`	No	Canvas 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_id`	No	Narrow 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.

Changed12 schema fields changedv1.18.15Oct 14, 2026

addedInput schema / properties / assignment_group_id / anyOf

Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]

removedInput schema / properties / assignment_group_id / exclusiveMinimum
```
Removed value: -0
```
removedInput schema / properties / assignment_group_id / maximum
```
Removed value: -9007199254740991
```
removedInput schema / properties / assignment_group_id / type
```
Removed value: -"integer"
```

addedInput schema / properties / course_id / anyOf

Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]

removedInput schema / properties / course_id / exclusiveMinimum
```
Removed value: -0
```
removedInput schema / properties / course_id / maximum
```
Removed value: -9007199254740991
```
removedInput schema / properties / course_id / type
```
Removed value: -"integer"
```

addedInput schema / properties / student_id / anyOf

Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]

removedInput schema / properties / student_id / exclusiveMinimum
```
Removed value: -0
```
removedInput schema / properties / student_id / maximum
```
Removed value: -9007199254740991
```
removedInput schema / properties / student_id / type
```
Removed value: -"integer"
```

Changed1 schema field changedv1.18.11Sep 10, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

Addedv1.18.3Jul 24, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnly/non-destructive annotations, it discloses substantial behavior: V1 processes one student per call, curves and fudge points cannot be reflected and a caveat is emitted above a 0.5 pp discrepancy, grading-period courses reconcile against the cross-period grade, and pseudonymized student IDs require a prior resolve_pseudonym call. This is unusually rich behavioral context.

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?

The output contents are front-loaded in the first sentence, followed by a use-case sentence and a scannable Limitations list. It is dense and a little long, but nearly every clause carries actionable information, so waste is minimal.

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?

With no output schema, the description compensates by enumerating exactly what is returned (weights, drop rules, per-group breakdowns, letter grade, reconciliation), plus scope, limits, and prerequisites. An agent has everything needed to call and interpret it correctly.

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 100%, so the baseline is 3, but the description adds cross-reference value the schema lacks: the pseudonymization prerequisite for student_id and the note that omitting it computes for the authenticated user. It largely restates assignment_group_id narrowing already covered by the schema, keeping it below 5.

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 ('Recomputes and explains the weighted course grade for a student') and enumerates the exact outputs (group weights, drop rules, per-group breakdowns, letter grade mapping, Canvas reconciliation). An agent can distinguish this from get_my_grades, project_grade, and explain_grading_policy without opening any schema.

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 use case: 'Use this when you need to verify that Canvas's displayed grade matches the rules, or to explain... how their grade was calculated.' The Limitations block supplies clear when-not conditions (one student per call, curves/fudge points unavailable, pseudonymization prerequisite). It stops short of naming a specific sibling alternative for those excluded cases, so it is strong but not fully routing.

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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Explain Grade

Instructions

Input Schema

Schema Changelog

TDQS

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