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vishalsachdev

Canvas MCP Server

by vishalsachdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Get Peer Review Completion Analytics

get_peer_review_completion_analytics
Read-only

Track peer review completion by course assignment, showing per-student status and summary statistics to identify who has finished and who needs follow-up.

Instructions

Get peer review completion analytics with student-level breakdown and summary stats.

Input Schema

NameRequiredDescriptionDefault
assignment_idYesCanvas assignment ID
group_by_statusNoGroup students by completion status
course_identifierYesCourse code or Canvas ID
include_student_detailsNoInclude per-student breakdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.10.0
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "type": "object",
-  "x-fastmcp-wrap-result": true
-}New value: +null
  2. Changed13 schema fields changedv1.8.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / assignment_id / description
      Added value: +"Canvas assignment ID"
    • removedInput schema / properties / assignment_id / title
      Removed value: -"Assignment Id"
    • addedInput schema / properties / course_identifier / description
      Added value: +"Course code or Canvas ID"
    • removedInput schema / properties / course_identifier / title
      Removed value: -"Course Identifier"
    • addedInput schema / properties / group_by_status / description
      Added value: +"Group students by completion status"
    • removedInput schema / properties / group_by_status / title
      Removed value: -"Group By Status"
    • addedInput schema / properties / include_student_details / description
      Added value: +"Include per-student breakdown"
    • removedInput schema / properties / include_student_details / title
      Removed value: -"Include Student Details"
    • removedInput schema / title
      Removed value: -"get_peer_review_completion_analyticsArguments"
    • removedOutput schema / properties / result / title
      Removed value: -"Result"
    • removedOutput schema / title
      Removed value: -"get_peer_review_completion_analyticsOutput"
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  3. Changed1 schema field changedv1.0.0
    • addedInput schema / title
      Added value: +"get_peer_review_completion_analyticsArguments"
  4. First observed

TDQS

A4/5.0
Behavior4/5

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

The annotations define the safety boundary (read-only). The description adds crucial behavioral context by specifying the data granularity—that the output includes 'student-level breakdown and summary stats'—which is a core functional characteristic of the tool.

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 extremely concise, consisting of a single, information-dense sentence. It front-loads the essential nature of the tool without any filler.

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?

Given the absence of an output schema, the description is quite complete, as it defines the scope and level of detail of the analytics. It provides all necessary high-level information for an agent to decide if this is the right function to call.

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?

Since the input schema provides 100% coverage of all parameters, the description does not need to add semantic meaning to the input fields. A baseline score of 3 is appropriate for tools with high schema coverage.

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 tool name, title, and description all clearly state the purpose: retrieving 'peer review completion analytics.' It successfully distinguishes its function from general assignment or student analytics by being highly specific to peer review processes.

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 description states *what* the tool does, but not explicitly *when* to use it versus similar tools like `get_assignment_analytics`. It relies on the agent having contextual knowledge of the required course and assignment inputs.

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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