generate_peer_review_report
Generate peer review completion reports with executive summaries, analytics, and actionable recommendations for Canvas assignments.
Instructions
Generate comprehensive peer review completion report with executive summary, detailed analytics, and actionable follow-up recommendations.
Args:
course_identifier: Canvas course code (e.g., badm_554_120251_246794) or ID
assignment_id: Canvas assignment ID
report_format: Report format (markdown, csv, json)
include_executive_summary: Include executive summary
include_student_details: Include student details
include_action_items: Include action items
include_timeline_analysis: Include timeline analysis
save_to_file: Save report to local file
filename: Custom filename for saved report
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| course_identifier | Yes | ||
| assignment_id | Yes | ||
| report_format | No | markdown | |
| include_executive_summary | No | ||
| include_student_details | No | ||
| include_action_items | No | ||
| include_timeline_analysis | No | ||
| save_to_file | No | ||
| filename | No |
Implementation Reference
- Primary MCP tool handler for generate_peer_review_report. Decorated with @mcp.tool(), validates params, delegates to PeerReviewAnalyzer.generate_report, handles output formatting and optional file saving.@mcp.tool() @validate_params async def generate_peer_review_report( course_identifier: str | int, assignment_id: str | int, report_format: str = "markdown", include_executive_summary: bool = True, include_student_details: bool = True, include_action_items: bool = True, include_timeline_analysis: bool = True, save_to_file: bool = False, filename: str = None ) -> str: """Generate comprehensive peer review completion report with executive summary, detailed analytics, and actionable follow-up recommendations. Args: course_identifier: Canvas course code (e.g., badm_554_120251_246794) or ID assignment_id: Canvas assignment ID report_format: Report format (markdown, csv, json) include_executive_summary: Include executive summary include_student_details: Include student details include_action_items: Include action items include_timeline_analysis: Include timeline analysis save_to_file: Save report to local file filename: Custom filename for saved report """ try: course_id = await get_course_id(course_identifier) analyzer = PeerReviewAnalyzer() result = await analyzer.generate_report( course_id=course_id, assignment_id=int(assignment_id), report_format=report_format, include_executive_summary=include_executive_summary, include_student_details=include_student_details, include_action_items=include_action_items, include_timeline_analysis=include_timeline_analysis ) if "error" in result: return f"Error generating peer review report: {result['error']}" # Handle file saving if requested if save_to_file and "report" in result: import os from datetime import datetime if not filename: timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"peer_review_report_{assignment_id}_{timestamp}.{report_format}" try: # Save to current working directory with open(filename, 'w', encoding='utf-8') as f: f.write(result["report"]) result["saved_to"] = os.path.abspath(filename) except Exception as save_error: result["save_error"] = f"Failed to save file: {str(save_error)}" if report_format in ["csv", "markdown"]: return result.get("report", json.dumps(result, indent=2)) else: return json.dumps(result, indent=2) except Exception as e: return f"Error in generate_peer_review_report: {str(e)}"
- Core helper method in PeerReviewAnalyzer class that implements the report generation logic by aggregating completion analytics and assignment data, then dispatching to format-specific generators.async def generate_report( self, course_id: int, assignment_id: int, report_format: str = "markdown", include_executive_summary: bool = True, include_student_details: bool = True, include_action_items: bool = True, include_timeline_analysis: bool = True ) -> dict[str, Any]: """Generate comprehensive peer review completion report.""" try: # Get analytics data analytics = await self.get_completion_analytics( course_id, assignment_id, include_student_details=True ) if "error" in analytics: return analytics # Get assignment info assignments_data = await self.get_assignments(course_id, assignment_id) if "error" in assignments_data: return assignments_data assignment_info = assignments_data["assignment_info"] if report_format == "markdown": return self._generate_markdown_report( analytics, assignment_info, include_executive_summary, include_student_details, include_action_items, include_timeline_analysis ) elif report_format == "csv": return self._generate_csv_report(analytics, assignment_info) elif report_format == "json": return { "assignment_info": assignment_info, "analytics": analytics, "generated_at": datetime.datetime.now().isoformat() } else: return {"error": f"Unsupported report format: {report_format}"} except Exception as e: return {"error": f"Exception in generate_report: {str(e)}"}
- src/canvas_mcp/server.py:42-64 (registration)Registration function that calls register_peer_review_tools(mcp) at line 52, which in turn defines and registers the generate_peer_review_report tool.def register_all_tools(mcp: FastMCP) -> None: """Register all MCP tools, resources, and prompts.""" log_info("Registering Canvas MCP tools...") # Register tools by category register_course_tools(mcp) register_assignment_tools(mcp) register_discussion_tools(mcp) register_other_tools(mcp) register_rubric_tools(mcp) register_peer_review_tools(mcp) register_peer_review_comment_tools(mcp) register_messaging_tools(mcp) register_student_tools(mcp) register_accessibility_tools(mcp) register_discovery_tools(mcp) register_code_execution_tools(mcp) # Register resources and prompts register_resources_and_prompts(mcp) log_info("All Canvas MCP tools registered successfully!")