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vishalsachdev

Canvas MCP Server

by vishalsachdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Analyze Peer Review Quality

analyze_peer_review_quality
Read-only

Analyze the quality and content of peer review comments to assess feedback effectiveness and support continuous improvement in peer assessment.

Instructions

Analyze the quality and content of peer review comments.

Input 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

C2.6/5.0
Behavior2/5

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

readOnlyHint=true already tells the agent this is a safe read, so the description should add analysis behavior: what dimensions are scored, whether it scans all peer reviews in the assignment, and what the result looks like. It adds none of that, offering only a restatement of the name.

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 short sentence with no filler, front-loaded with the action. Its brevity is a virtue here, though it tilts toward under-specification rather than efficiency.

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?

There is no output schema and no explanation of what the analysis yields, how it scopes to the assignment's peer reviews, or what custom criteria do. For a four-parameter analytical tool with many similar siblings, that leaves too much unspecified.

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 four parameters are already documented in the schema and a baseline of 3 applies. The description contributes no extra meaning for analysis_criteria (custom JSON criteria) or generate_report, but it is not required to under the coverage rule.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The verb+resource ("Analyze ... peer review comments") is identifiable, but "quality and content" is vague about what is actually produced. It also does nothing to distinguish it from closely overlapping siblings such as identify_problematic_peer_reviews, get_peer_review_comments, and generate_peer_review_report.

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 statement of when to use this tool, no prerequisites, and no mention of the many sibling tools that handle adjacent peer-review tasks. The agent must infer the choice from the name alone.

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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