Canvas MCP Server

Instructions

Implementation Reference

• src/canvas_mcp/tools/peer_review_comments.py:147-264 (handler)

  The core handler function implementing the logic for the 'extract_peer_review_dataset' tool. It fetches peer review comments, optionally analyzes them, and exports to CSV or JSON.
  @mcp.tool() @validate_params async def extract_peer_review_dataset( course_identifier: str | int, assignment_id: str | int, output_format: str = "csv", include_analytics: bool = True, anonymize_data: bool = True, save_locally: bool = True, filename: str | None = None ) -> str: """ Export all peer review data in various formats for analysis. Args: course_identifier: Canvas course code or ID assignment_id: Canvas assignment ID output_format: Output format (csv, json, xlsx) include_analytics: Include quality analytics in output anonymize_data: Anonymize student data save_locally: Save file locally filename: Custom filename (optional) """ try: course_id = await get_course_id(course_identifier) analyzer = PeerReviewCommentAnalyzer() # Get the comment data comments_data = await analyzer.get_peer_review_comments( course_id=course_id, assignment_id=int(assignment_id), include_reviewer_info=True, include_reviewee_info=True, include_submission_context=True, anonymize_students=anonymize_data ) if "error" in comments_data: return f"Error getting comments data: {comments_data['error']}" # Generate filename if not provided if not filename: assignment_name = comments_data.get("assignment_info", {}).get("assignment_name", "assignment") safe_name = "".join(c for c in assignment_name if c.isalnum() or c in (' ', '-', '_')).rstrip() filename = f"peer_reviews_{safe_name}_{assignment_id}" # Include analytics if requested if include_analytics: analytics_data = await analyzer.analyze_peer_review_quality( course_id=course_id, assignment_id=int(assignment_id) ) if "error" not in analytics_data: comments_data["quality_analytics"] = analytics_data # Export based on format if output_format.lower() == "json": output_filename = f"{filename}.json" if save_locally: with open(output_filename, 'w', encoding='utf-8') as f: json.dump(comments_data, f, indent=2, ensure_ascii=False) return f"Data exported to {output_filename}" else: return json.dumps(comments_data, indent=2) elif output_format.lower() == "csv": output_filename = f"{filename}.csv" if save_locally: with open(output_filename, 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f) # Write header writer.writerow([ 'review_id', 'reviewer_id', 'reviewer_name', 'reviewee_id', 'reviewee_name', 'comment_text', 'word_count', 'character_count', 'timestamp' ]) # Write data for review in comments_data.get("peer_reviews", []): reviewer = review.get("reviewer", {}) reviewee = review.get("reviewee", {}) content = review.get("review_content", {}) writer.writerow([ review.get("review_id", ""), reviewer.get("student_id", ""), reviewer.get("student_name", ""), reviewee.get("student_id", ""), reviewee.get("student_name", ""), content.get("comment_text", ""), content.get("word_count", 0), content.get("character_count", 0), content.get("timestamp", "") ]) return f"Data exported to {output_filename}" else: # Return CSV as string csv_lines = [] csv_lines.append("review_id,reviewer_id,reviewer_name,reviewee_id,reviewee_name,comment_text,word_count,character_count,timestamp") for review in comments_data.get("peer_reviews", []): reviewer = review.get("reviewer", {}) reviewee = review.get("reviewee", {}) content = review.get("review_content", {}) # Escape quotes in comment text comment_text = content.get("comment_text", "").replace('"', '""') csv_lines.append(f'"{review.get("review_id", "")}","{reviewer.get("student_id", "")}","{reviewer.get("student_name", "")}","{reviewee.get("student_id", "")}","{reviewee.get("student_name", "")}","{comment_text}",{content.get("word_count", 0)},{content.get("character_count", 0)},"{content.get("timestamp", "")}"') return "\n".join(csv_lines) else: return f"Error: Unsupported output format '{output_format}'. Supported formats: csv, json" except Exception as e: return f"Error in extract_peer_review_dataset: {str(e)}"
• src/canvas_mcp/server.py:53-53 (registration)

  The registration call for the peer review comments tools module, which includes the 'extract_peer_review_dataset' tool.
  register_peer_review_comment_tools(mcp)
• src/canvas_mcp/tools/peer_review_comments.py:149-157 (schema)

  Input schema defined by function parameters and type hints, validated by @validate_params decorator.
  async def extract_peer_review_dataset( course_identifier: str | int, assignment_id: str | int, output_format: str = "csv", include_analytics: bool = True, anonymize_data: bool = True, save_locally: bool = True, filename: str | None = None ) -> str: