create_student_anonymization_map

Generate a secure CSV file that maps real student data to anonymous IDs for a Canvas course, creating a de-anonymization key for faculty to identify students while maintaining privacy.

Instructions

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`course_identifier`	Yes

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)
```

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