| name | canvas-peer-review-manager |
| description | Educator peer review management for Canvas LMS. Tracks completion rates, analyzes comment quality, flags problematic reviews, sends targeted reminders, and generates instructor-ready reports. Trigger phrases include "peer review status", "how are peer reviews going", "who hasn't reviewed", "review quality", or any peer review follow-up task. |
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:
| Metric | What It Tells You |
|---|
| Completion rate | Overall health of the peer review cycle |
| "None complete" count | Students who haven't started -- highest priority for reminders |
| "Partial complete" count | Students who started but didn't finish |
| Per-student breakdown | Exactly who needs follow-up |
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 outputinclude_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 commentinclude_reviewee_info=true -- who received the commentanonymize_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 completedpriority_filter="medium" -- students with partial completionpriority_filter="all" -- everyone who needs follow-updays_threshold=3 -- adjusts urgency calculation based on days since assignment
8. Send Reminders
Always use a dry run or review step before sending messages.
For targeted direct Inbox messages, call send_peer_review_inbox_messages with:
recipient_ids -- list of Canvas user IDs from the analytics resultscustom_message -- optional custom text (a default template is used if omitted)subject_prefix -- defaults to "Peer Review Reminder"
Example flow:
- Get incomplete reviewers from step 2
- Extract their user IDs
- Review the recipient list with the user
- Send reminders after confirmation
For a fully automated pipeline, call send_peer_review_followup_campaign with just the course identifier and assignment ID. This tool:
- Runs completion analytics automatically
- Segments students into "urgent" (none complete) and "partial" groups
- Sends appropriately toned reminders to each group
- 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 exportanonymize_data=true -- recommended for sharing or archivalsave_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 reviewsreport_type="summary" -- executive overview onlyreport_type="individual" -- per-student breakdowninclude_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
| Tool | Purpose |
|---|
list_courses | Discover active courses |
list_assignments | Find assignments with peer reviews enabled |
get_peer_review_assignments | Full reviewer-to-reviewee mapping |
get_peer_review_completion_analytics | Completion rates and per-student breakdown |
get_peer_review_comments | Extract actual comment text |
analyze_peer_review_quality | Quality metrics (scores, word counts, constructiveness) |
identify_problematic_peer_reviews | Flag low-quality or empty reviews |
get_peer_review_followup_list | Prioritized list of students needing follow-up |
send_peer_review_inbox_messages | Send targeted direct Canvas Inbox messages |
send_peer_review_followup_campaign | Automated analytics-to-messaging pipeline |
extract_peer_review_dataset | Export data as CSV or JSON |
generate_peer_review_feedback_report | Quality-focused instructor report |
generate_peer_review_report | Completion-focused instructor report |
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)
- Student_e4f03a9 (2 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.
- Use dry runs -- When testing workflows, start with a single recipient or confirm the output of analytics tools before acting on the data.
- 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 into one call -- powerful but sends real messages. Use it only after confirming intent with the instructor.
- 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.
