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Canvas MCP Server

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extract_peer_review_dataset

Read-only

Export peer review data for analysis. Choose CSV, JSON, or XLSX output, anonymize student data, and include quality analytics.

Instructions

Export all peer review data in various formats for analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameNoCustom filename
save_locallyNoSave file locally
assignment_idYesCanvas assignment ID
output_formatNoOutput format (csv, json, xlsx)csv
anonymize_dataNoAnonymize student data
course_identifierYesCourse code or Canvas ID
include_analyticsNoInclude quality analytics

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. Changed19 schema fields changedv1.8.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / anonymize_data / description
      Added value: +"Anonymize student data"
    • removedInput schema / properties / anonymize_data / title
      Removed value: -"Anonymize Data"
    • 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 / filename / description
      Added value: +"Custom filename"
    • removedInput schema / properties / filename / title
      Removed value: -"Filename"
    • addedInput schema / properties / include_analytics / description
      Added value: +"Include quality analytics"
    • removedInput schema / properties / include_analytics / title
      Removed value: -"Include Analytics"
    • addedInput schema / properties / output_format / description
      Added value: +"Output format (csv, json, xlsx)"
    • removedInput schema / properties / output_format / title
      Removed value: -"Output Format"
    • addedInput schema / properties / save_locally / description
      Added value: +"Save file locally"
    • removedInput schema / properties / save_locally / title
      Removed value: -"Save Locally"
    • removedInput schema / title
      Removed value: -"extract_peer_review_datasetArguments"
    • removedOutput schema / properties / result / title
      Removed value: -"Result"
    • removedOutput schema / title
      Removed value: -"extract_peer_review_datasetOutput"
    • addedOutput schema / x-fastmcp-wrap-result
      Added value: +true
  3. Changed1 schema field changedv1.0.0
    • addedInput schema / title
      Added value: +"extract_peer_review_datasetArguments"
  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=true annotation is consistent with the export description, so no contradiction. However, the description adds minimal behavioral context beyond the annotation—it does not disclose file output behavior, default anonymization, or whether the tool returns data directly. The scope 'all peer review data' is a useful addition.

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 short sentence with no redundant content. It is front-loaded with the action ('Export') and communicates the core purpose efficiently.

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?

Given the tool's complexity (7 parameters, no output schema, and many peer-review siblings), the description is too terse. It does not specify what 'all peer review data' includes, how the export is delivered (file vs. data), or when this bulk export is appropriate. The absence of an output schema places more burden on the description, which it does not satisfy.

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 coverage is 100% and all parameters have descriptions, so the baseline is 3. The description's 'various formats' vaguely anticipates the output_format parameter but adds no syntax or default details beyond what the schema already provides.

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 clearly states the action (export), resource (all peer review data), and purpose (for analysis). It implies a bulk extraction tool, which distinguishes it from siblings like get_peer_review_comments or generate_peer_review_report, though it does not explicitly name those alternatives.

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?

No explicit guidance on when to use this tool versus the many peer-review sibling tools. The phrase 'for analysis' hints at a use case but does not state exclusions or mention alternatives such as get_peer_review_completion_analytics or generate_peer_review_report.

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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