Canvas MCP Server

Instructions

    This tool generates a de-anonymization key that allows faculty to identify students
    from their anonymous IDs. The file is saved locally and should be kept secure.

    Args:
        course_identifier: The Canvas course code (e.g., badm_554_120251_246794) or ID
    

Implementation Reference

• src/canvas_mcp/tools/other_tools.py:574-660 (handler)

  The primary handler function for the 'create_student_anonymization_map' tool. Decorated with @mcp.tool() and @validate_params, it fetches students from the Canvas course, generates anonymous IDs, and writes a secure local CSV mapping file.

  @mcp.tool()
  @validate_params
  async def create_student_anonymization_map(course_identifier: str | int) -> str:
      """Create a local CSV file mapping real student data to anonymous IDs for a course.
  
      This tool generates a de-anonymization key that allows faculty to identify students
      from their anonymous IDs. The file is saved locally and should be kept secure.
  
      Args:
          course_identifier: The Canvas course code (e.g., badm_554_120251_246794) or ID
      """
      import csv
      from pathlib import Path
  
      from ..core.anonymization import generate_anonymous_id
  
      course_id = await get_course_id(course_identifier)
  
      # Get all students in the course
      params = {
          "enrollment_type[]": "student",
          "include[]": ["email"],
          "per_page": 100
      }
  
      students = await fetch_all_paginated_results(
          f"/courses/{course_id}/users", params
      )
  
      if isinstance(students, dict) and "error" in students:
          return f"Error fetching students: {students['error']}"
  
      if not students:
          return f"No students found for course {course_identifier}."
  
      # Create local_maps directory if it doesn't exist
      maps_dir = Path("local_maps")
      maps_dir.mkdir(exist_ok=True)
  
      # Generate filename with course identifier
      course_display = await get_course_code(course_id) or str(course_identifier)
      safe_course_name = "".join(c for c in course_display if c.isalnum() or c in ("-", "_"))
      filename = f"anonymization_map_{safe_course_name}.csv"
      filepath = maps_dir / filename
  
      # Create mapping data
      mapping_data = []
      for student in students:
          real_id = student.get("id")
          real_name = student.get("name", "Unknown")
          real_email = student.get("email", "No email")
  
          # Generate the same anonymous ID that would be used by the anonymization system
          anonymous_id = generate_anonymous_id(real_id, prefix="Student")
  
          mapping_data.append({
              "real_name": real_name,
              "real_id": real_id,
              "real_email": real_email,
              "anonymous_id": anonymous_id
          })
  
      # Write to CSV file
      try:
          with open(filepath, 'w', newline='', encoding='utf-8') as csvfile:
              fieldnames = ["real_name", "real_id", "real_email", "anonymous_id"]
              writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
  
              writer.writeheader()
              writer.writerows(mapping_data)
  
          result = "✅ Student anonymization map created successfully!\n\n"
          result += f"📁 File location: {filepath}\n"
          result += f"👥 Students mapped: {len(mapping_data)}\n"
          result += f"🏫 Course: {course_display}\n\n"
          result += "⚠️ **SECURITY WARNING:**\n"
          result += "This file contains sensitive student information and should be:\n"
          result += "• Kept secure and not shared\n"
          result += "• Deleted when no longer needed\n"
          result += "• Never committed to version control\n\n"
          result += "📋 File format: CSV with columns real_name, real_id, real_email, anonymous_id\n"
          result += "🔍 Use this file to identify students from their anonymous IDs in tool outputs."
  
          return result
  
      except Exception as e:
          return f"Error creating anonymization map: {str(e)}"

• src/canvas_mcp/server.py:50-50 (registration)

  Top-level registration call within register_all_tools(mcp) that invokes register_other_tools to register this tool along with other tools in the 'other_tools' module.

  register_other_tools(mcp)