canvas-peer-review-manager

Canvas Peer Review Manager

A complete peer review management workflow for educators using Canvas LMS. Monitor completion, analyze quality, identify students who need follow-up, send reminders, and export data -- all through MCP tool calls against the Canvas API.

Prerequisites

• Canvas MCP server must be running and connected to the agent's MCP client (e.g., Claude Code, Cursor, Codex, OpenCode).
• The authenticated user must have an educator or instructor role in the target Canvas course.
• The assignment must have peer reviews enabled in Canvas (either manual or automatic assignment).
• FERPA-conscious handling: Set ENABLE_DATA_ANONYMIZATION=true in the Canvas MCP server environment to anonymize supported student identity fields. When enabled, names render as Student_xxxxxxxx hashes while preserving functional user IDs for messaging. This control does not by itself establish compliance.

Steps

1. Identify the Assignment

Ask the user which course and assignment to manage peer reviews for. Accept a course code, Canvas ID, or course name, plus an assignment name or ID.

If the user does not specify, prompt:

Which course and assignment would you like to check peer reviews for?

Use list_courses and list_assignments to help the user find the right identifiers.

2. Check Peer Review Completion

Call get_peer_review_completion_analytics with the course identifier and assignment ID. This returns:

• Overall completion rate (percentage)
• Number of students with all reviews complete, partial, and none complete
• Per-student breakdown showing completed vs. assigned reviews

Key data points to surface:

3. Review the Assignment Mapping

If the user wants to understand who is reviewing whom, call get_peer_review_assignments with:

• include_names=true for human-readable output
• include_submission_details=true for submission context

This shows the full reviewer-to-reviewee mapping with completion status.

4. Extract and Read Comments

Call get_peer_review_comments to retrieve actual comment text. Parameters:

• include_reviewer_info=true -- who wrote the comment
• include_reviewee_info=true -- who received the comment
• anonymize_students=true -- recommended when sharing results or working with sensitive data

This reveals what students actually wrote in their reviews.

5. Analyze Comment Quality

Call analyze_peer_review_quality to generate quality metrics across all reviews. The analysis includes:

• Average quality score (1-5 scale)
• Word count statistics (mean, median, range)
• Constructiveness analysis (constructive feedback vs. generic comments vs. specific suggestions)
• Sentiment distribution (positive, neutral, negative)
• Flagged reviews that fall below quality thresholds

Optionally pass analysis_criteria as a JSON string to customize what counts as high/low quality.

6. Flag Problematic Reviews

Call identify_problematic_peer_reviews to automatically flag reviews needing instructor attention. Flagging criteria include:

• Very short or empty comments
• Generic responses (e.g., "looks good", "nice work")
• Lack of constructive feedback
• Potential copy-paste or identical reviews

Pass custom criteria as a JSON string to override default thresholds.

7. Get the Follow-up List

Call get_peer_review_followup_list to get a prioritized list of students requiring action:

• priority_filter="urgent" -- students with zero reviews completed
• priority_filter="medium" -- students with partial completion
• priority_filter="all" -- everyone who needs follow-up
• days_threshold=3 -- adjusts urgency calculation based on days since assignment

8. Send Reminders

Both send tools are two-call: the first call returns a preview and a confirmation_token and sends nothing; show the preview to the instructor, then call again with the token (and identical arguments) to send.

For targeted direct Inbox messages, call send_peer_review_inbox_messages with:

• recipient_ids -- list of Canvas user IDs from the analytics results
• custom_message -- optional custom text (a default template is used if omitted)
• subject_prefix -- defaults to "Peer Review Reminder"

Example flow:

1. Get incomplete reviewers from step 2
2. Extract their user IDs
3. Review the recipient list with the user
4. Send reminders after confirmation

For an automated pipeline, call send_peer_review_followup_campaign with the course identifier and assignment ID; the first call returns analytics plus a preview of urgent vs. gentle recipients and a token. This tool:

1. Runs completion analytics automatically
2. Segments students into "urgent" (none complete) and "partial" groups
3. Sends the reminders only on the confirming call with the token
4. Returns combined analytics and messaging results

Warning: The campaign tool sends real messages. Always confirm with the instructor before running it.

9. Export Data

Call extract_peer_review_dataset to export all peer review data for external analysis:

• output_format="csv" or output_format="json"
• include_analytics=true -- appends quality metrics to the export
• anonymize_data=true -- recommended for sharing or archival
• save_locally=true -- saves to a local file; set to false to return data inline

10. Generate Instructor Reports

Call generate_peer_review_feedback_report for a formatted, shareable report:

• report_type="comprehensive" -- full analysis with samples of low-quality reviews
• report_type="summary" -- executive overview only
• report_type="individual" -- per-student breakdown
• include_student_names=false -- recommended for privacy-conscious reporting

For a completion-focused report (rather than quality-focused), use generate_peer_review_report with options for executive summary, student details, action items, and timeline analysis. This report can be saved to a file with save_to_file=true.

Use Cases

"How are peer reviews going?" Run steps 1-2. Present completion rate, highlight any concerning patterns (e.g., "Only 60% complete, 8 students haven't started").

"Who hasn't done their reviews?" Run steps 1-2, then step 7 with priority_filter="urgent". List the students who need follow-up.

"Are the reviews any good?" Run steps 4-6. Present quality scores, flag generic or low-effort reviews, and surface recommendations.

"Send reminders to stragglers" Run steps 1-2 to identify incomplete reviewers, then step 8. Always confirm the recipient list before sending.

"Give me a full report" Run steps 2, 5, 6, and 10. Combine completion analytics with quality analysis into a comprehensive instructor report.

"Export everything for my records" Run step 9 with output_format="csv" and anonymize_data=true for a privacy-conscious dataset.

MCP Tools Used

Example

User: "How are peer reviews going for Assignment 3 in BADM 350?"

Agent: Calls get_peer_review_completion_analytics and presents:

## Peer Review Status: Assignment 3

- **Completion rate:** 72% (23/32 students fully complete)
- **Partial:** 5 students (started but not finished)
- **Not started:** 4 students

### Students Needing Follow-up
**Not started (urgent):**
- Student_a8f7e23 (0 of 3 reviews done)
- Student_b2c91d4 (0 of 3 reviews done)
- Student_f5e67a1 (0 of 3 reviews done)
- Student_d9c34b2 (0 of 3 reviews done)

**Partial (needs nudge):**
- Student_c1d82e5 (1 of 3 reviews done)

User: "Send reminders to the ones who haven't started"

Agent: Confirms the 4 recipients, then calls send_peer_review_inbox_messages with their user IDs.

User: "Now check if the completed reviews are any good"

Agent: Calls analyze_peer_review_quality and presents quality scores, flags 3 reviews as too short, and recommends the instructor follow up with specific students.

Safety Guidelines

• Confirm before sending -- Always present the recipient list and message content to the instructor before calling any messaging tool.
• Anonymize by default -- Use anonymize_students=true or anonymize_data=true when reviewing data in shared contexts.
• Respect rate limits -- The Canvas API allows roughly 700 requests per 10 minutes. For large courses, the messaging tools send messages sequentially with built-in delays.
• FERPA-conscious handling -- Never display student names in logs, shared screens, or exported files unless the instructor has explicitly confirmed the context is appropriate.

Notes

• Peer reviews must be enabled on the assignment in Canvas before any of these tools return data.
• The send_peer_review_followup_campaign tool combines analytics and messaging: the first call previews recipients and returns a token, and the second call (with the token) sends real messages. Make the second call only after the instructor approves the preview.
• Quality analysis uses heuristics (word count, keyword matching, sentiment). It identifies likely low-quality reviews but is not a substitute for instructor judgment.
• This skill pairs well with canvas-morning-check for a full course health overview that includes peer review status alongside submission rates and grade distribution.