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vishalsachdev

Canvas MCP Server

by vishalsachdev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Extract Peer Review Dataset

extract_peer_review_dataset
DestructiveIdempotent

Export peer review data from Canvas assignments as CSV, JSON, or XLSX files for analysis, with options to anonymize student information and include quality analytics.

Instructions

Export all peer review data in various formats for analysis.

Input 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

C2.9/5.0
Behavior2/5

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

Annotations declare idempotentHint=true and destructiveHint=true, but the description adds no context about what is destructive: nothing says it writes a file to local disk (save_locally defaults true), nothing mentions the default anonymization of student data, and nothing warns about export volume or permissions. With a destructive hint and no explanatory text, the description leaves the risky behavior opaque.

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, front-loaded sentence with zero redundancy. It is efficient, though the brevity edges toward under-specification rather than tightness.

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?

This is a 7-parameter bulk export that writes files and handles sensitive student data, with no output schema. The description says nothing about the default-on anonymization, local file creation, or what 'all peer review data' contains, so an agent cannot predict the operation's consequences from the text.

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 seven parameters are already documented in the schema. The description only echoes this with 'various formats', adding no meaning about format choices, filename behavior, or how anonymize_data/include_analytics affect output. Baseline 3 applies.

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 (Export) and resource (peer review data) with scope (all) and purpose (for analysis). However, it does not differentiate itself from the many peer-review siblings such as generate_peer_review_report, analyze_peer_review_quality, or get_peer_review_comments, so an agent must infer how this differs.

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 on when to use this export over the report/analytics siblings, no mention of prerequisites, and no exclusions. The only hint is 'for analysis', which does not route the agent among the numerous peer-review tools.

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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