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Canvas MCP Server

by vishalsachdev
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Python
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analyze_peer_review_quality

Read-only

Evaluate peer review comments for quality and content. Generate a detailed report to uncover feedback strengths and areas needing improvement in student reviews.

Instructions

Analyze the quality and content of peer review comments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assignment_idYesCanvas assignment ID
generate_reportNoGenerate detailed analysis report
analysis_criteriaNoJSON string of custom criteria
course_identifierYesCourse code or Canvas ID

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 / analysis_criteria / description
      Added value: +"JSON string of custom criteria"
    • removedInput schema / properties / analysis_criteria / title
      Removed value: -"Analysis Criteria"
    • 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 / generate_report / description
      Added value: +"Generate detailed analysis report"
    • removedInput schema / properties / generate_report / title
      Removed value: -"Generate Report"
    • removedInput schema / title
      Removed value: -"analyze_peer_review_qualityArguments"
    • removedOutput schema / properties / result / title
      Removed value: -"Result"
    • removedOutput schema / title
      Removed value: -"analyze_peer_review_qualityOutput"
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  3. Changed1 schema field changedv1.0.0
    • addedInput schema / title
      Added value: +"analyze_peer_review_qualityArguments"
  4. First observed

TDQS

B3.1/5.0
Behavior3/5

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

The readOnlyHint annotation already covers safety, and the description's 'Analyze' wording is consistent with a read-only operation. The description adds the scope of analysis ('quality and content') but does not disclose report generation behavior, output format, or any other execution traits beyond the schema.

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 description is a single front-loaded sentence with no redundant or filler content. It is concise, though slightly terse given the tool's four parameters and the absence of an output schema.

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

Completeness2/5

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

The tool has no output schema and sits among many closely related peer-review tools, but the description does not explain what the analysis returns, how generate_report affects behavior, or how analysis_criteria customizes results. This leaves significant gaps for an agent deciding how to invoke and interpret the tool correctly.

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 four parameters are already documented. The description adds no semantic detail about course_identifier, assignment_id, analysis_criteria, or generate_report, so it does not exceed the baseline established by 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?

The description uses a specific verb ('Analyze') and resource ('peer review comments') to convey a clear read-only analysis action, including the dual object of 'quality and content.' However, it does not differentiate from overlapping siblings like get_peer_review_comments or identify_problematic_peer_reviews, which likely analyze similar data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance about when to use this tool versus alternatives such as generate_peer_review_report, identify_problematic_peer_reviews, or get_peer_review_comments. The description states only what the tool does, not when it should be preferred or excluded.

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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