---
name: Canvas Peer Review Manager
slug: canvas-peer-review-manager
category: Automation
description: Canvas Peer Review Manager tracks peer review completion in Canvas LMS, analyzes comment quality, flags problematic reviews, and generates instructor reports. Use it to follow up on unfinished or low-quality peer reviews.
github: "https://github.com/vishalsachdev/canvas-mcp/tree/main/skills/canvas-peer-review-manager"
language: Python
stars: 226
forks: 76
install: "npx degit https://github.com/vishalsachdev/canvas-mcp/tree/main/skills/canvas-peer-review-manager ~/.claude/skills/canvas-peer-review-manager"
installs_to: ~/.claude/skills/canvas-peer-review-manager
source_path: skills/canvas-peer-review-manager/SKILL.md
collection_size: 8
category_size: 1860
collection_url: "https://dirskills.com/collections/vishalsachdev/canvas-mcp"
added: 2026-09-04T05:24:49.373Z
last_synced: 2026-09-04T05:24:49.373Z
canonical_url: "https://dirskills.com/skills/canvas-peer-review-manager"
---

# Canvas Peer Review Manager

Canvas Peer Review Manager tracks peer review completion in Canvas LMS, analyzes comment quality, flags problematic reviews, and generates instructor reports. Use it to follow up on unfinished or low-quality peer reviews.

**Install:**

```bash
npx degit https://github.com/vishalsachdev/canvas-mcp/tree/main/skills/canvas-peer-review-manager ~/.claude/skills/canvas-peer-review-manager
```

## README

# 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 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

**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 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 a fully automated pipeline, call `send_peer_review_followup_campaign` with just the course identifier and assignment ID. This tool:

1. Runs completion analytics automatically
2. Segments students into "urgent" (none complete) and "partial" groups
3. Sends appropriately toned reminders to each group
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

| 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.