Gradescope MCP Server

Instructions

Extracts ALL questions from the outline (prompt text, reference answers,
explanations) and saves to /tmp/gradescope-answerkey-{assignment_id}.md.
Run this ONCE before grading to avoid re-fetching question details.

Args:
    course_id: The Gradescope course ID.
    assignment_id: The assignment ID.

Implementation Reference

• src/gradescope_mcp/tools/grading_workflow.py:586-650 (handler)

  The actual implementation of the tool logic that prepares the answer key.

  def prepare_answer_key(course_id: str, assignment_id: str) -> str:
      """Prepare a complete answer key for an entire assignment.
  
      Extracts ALL questions from the assignment outline, including:
      - Question numbers, types, and weights
      - Prompt/question text (if available in structured data)
      - Explanation/reference answers (if provided by the instructor)
      - For questions without reference answers, generates a rubric-based draft
  
      Saves the result to /tmp/gradescope-answerkey-{assignment_id}.md.
      This file can then be referenced when grading individual submissions,
      saving context by not having to re-fetch question details each time.
  
      Args:
          course_id: The Gradescope course ID.
          assignment_id: The assignment ID.
      """
      if not course_id or not assignment_id:
          return "Error: course_id and assignment_id are required."
  
      try:
          questions = _fetch_assignment_questions(course_id, assignment_id)
      except AuthError as e:
          return f"Authentication error: {e}"
      except ValueError as e:
          return f"Error: {e}"
      except Exception as e:
          return f"Error preparing answer key: {e}"
  
      # Outline data is optional (scanned exams don't have AssignmentEditor)
      try:
          outline_props = _get_outline_data(course_id, assignment_id)
      except Exception:
          outline_props = {}
  
      outline_questions = outline_props.get("questions", {})
      assignment_info = outline_props.get("assignment", {})
      title = assignment_info.get("title", f"Assignment {assignment_id}")
  
      # Build a sorted list of questions
      question_list = []
      for qid, q in questions.items():
          parent_id = q.get("parent_id")
          q_data = {
              "id": qid,
              "title": q.get("title", ""),
              "weight": q.get("weight", 0),
              "type": q.get("type", "Unknown"),
              "parent_id": parent_id,
              "index": q.get("index", 0),
          }
  
          # Build label
          if parent_id and str(parent_id) in questions:
              parent = questions[str(parent_id)]
              q_data["label"] = f"Q{parent.get('index', '?')}.{q.get('index', '?')}"
          else:
              q_data["label"] = f"Q{q.get('index', '?')}"
  
          # Extract prompt text and explanation from outline
          outline_q = outline_questions.get(str(qid), {})
          prompt_parts = []
          explanation_parts = []
          for item in outline_q.get("content", []):
              item_type = item.get("type")

• src/gradescope_mcp/server.py:689-701 (registration)

  Registration of the 'tool_prepare_answer_key' tool as an MCP tool, which calls the 'prepare_answer_key' helper function.

  @mcp.tool()
  def tool_prepare_answer_key(course_id: str, assignment_id: str) -> str:
      """Prepare a complete answer key for an entire assignment.
  
      Extracts ALL questions from the outline (prompt text, reference answers,
      explanations) and saves to /tmp/gradescope-answerkey-{assignment_id}.md.
      Run this ONCE before grading to avoid re-fetching question details.
  
      Args:
          course_id: The Gradescope course ID.
          assignment_id: The assignment ID.
      """
      return prepare_answer_key(course_id, assignment_id)