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Canvas LMS MCP Server

by bruchris
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TypeScript
Hybrid

Get Outcome Mastery Distribution

get_outcome_mastery_distribution
Read-only

Get mastery distribution analytics for outcomes in a course, optionally filtered by students or outcomes.

Instructions

Get mastery distribution analytics for outcomes in a course, optionally filtered by students or outcomes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
excludeNoOptional exclusions for missing users or missing outcome results.
course_idYesThe Canvas course ID.
outcome_idsNoOptional outcome IDs to restrict the distribution results.
student_idsNoOptional Canvas student IDs (pass large IDs as strings) or SIS user IDs prefixed with "sis_user_id:".
add_defaultsNoInclude default mastery colors and levels when Canvas supports it.
only_assignment_alignmentsNoWhen including alignment distributions, limit them to assignments only.
show_unpublished_assignmentsNoInclude unpublished assignments in alignment distributions.
include_alignment_distributionsNoInclude contributing score distributions for alignments.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv1.18.15
    • addedInput schema / properties / course_id / anyOf
      Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]
    • removedInput schema / properties / course_id / type
      Removed value: -"number"
    • addedInput schema / properties / outcome_ids / items / anyOf
      Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]
    • removedInput schema / properties / outcome_ids / items / type
      Removed value: -"number"
    • changedInput schema / properties / student_ids / description
      Previous value: -"Optional Canvas student IDs or SIS user IDs prefixed with \"sis_user_id:\"."New value: +"Optional Canvas student IDs (pass large IDs as strings) or SIS user IDs prefixed with \"sis_user_id:\"."
    • addedInput schema / properties / student_ids / items / anyOf
      Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  },
+  {
+    "pattern": "^sis_user_id:[\\s\\S]+$",
+    "type": "string"
+  }
+]
    • removedInput schema / properties / student_ids / items / type
      Removed value: -[
-  "number",
-  "string"
-]
  2. 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"
  3. Changed2 schema fields changedv1.18.8
    • removedInput schema / properties / student_ids / items / anyOf
      Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "string"
-  }
-]
    • addedInput schema / properties / student_ids / items / type
      Added value: +[
+  "number",
+  "string"
+]
  4. First observedv1.18.0

TDQS

B3.2/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds nothing beyond that — no mention of pagination, response shape, permission requirements, or how 'mastery distribution' is computed, so it contributes little behavioral context of its own.

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?

A single front-loaded sentence with no wasted words, naming the resource and the optional filter up front. It is efficient, though terse enough to leave gaps rather than being optimally informative.

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

Completeness3/5

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

For an 8-parameter analytics tool with no output schema and minimal annotations, the description is thin: it never explains what a 'distribution' contains or how results are returned. It is adequate to identify the tool but not complete enough to anticipate its output.

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 all 8 parameters are fully documented in the schema; the baseline is 3. The description's 'filtered by students or outcomes' only loosely echoes the student_ids/outcome_ids parameters and adds no syntax or semantic detail beyond the schema.

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 ('get mastery distribution analytics for outcomes in a course') with the optional filter scope, so an agent can tell it apart from general outcome tools. It does not, however, explicitly differentiate itself from close siblings like get_outcome_results, get_outcome_rollups, or get_outcome_contributing_scores.

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 'optionally filtered by students or outcomes' implies when the filters matter, but there is no explicit when-to-use guidance or routing to alternatives such as get_outcome_results or get_outcome_rollups. Usage is only implied.

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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