Update server.py

server.py

@@ -1,60 +1,62 @@
-from mcp.server.fastmcp import FastMCP
-import logging
-import os
-
-# Initialize the server
-mcp = FastMCP("Math-Education-Server")
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-
-# Path to your collected resource
-MARKDOWN_FILE = r"resource\jemh114-min (1).md" 
-
-def get_local_resource():
-    """Helper to read the markdown file safely."""
-    if os.path.exists(MARKDOWN_FILE):
-        with open(MARKDOWN_FILE, "r", encoding="utf-8") as f:
-            return f.read()
-    return "Resource file not found."
-
-# --- Task 1: Generate a Summary ---
-@mcp.tool()
-async def generate_chapter_summary(chapter_name: str) -> str:
-    """
-    Provides the source material for a chapter. 
-    The AI should use this to create a summary.
-    """
-    raw_data = get_local_resource()
-    
-    # We use XML-style tags. Senior engineers do this because 
-    # LLMs (especially Claude) are trained to handle tagged data perfectly.
-    return f"""
-    <source_material>
-    {raw_data[:4000]} 
-    </source_material>
-
-    SYSTEM_INSTRUCTION: You are a Mathematics Expert. 
-    1. Read the text inside <source_material>.
-    2. Create a summary with 3 bullet points.
-    3. List all formulas found in LaTeX format.
-    4. Do not repeat the raw text; only provide the summary.
-    """
-
-# --- Task 2: Quiz Generator ---
-@mcp.tool()
-async def generate_quiz(chapter_name: str, difficulty: str = "medium") -> str:
-    """
-    Extracts formulas from the markdown and asks the LLM to build a quiz.
-    """
-    raw_data = get_local_resource()
-    
-    # Using the LLM's ability to pick out formulas from the raw text provided
-    return (
-        f"Here is the raw material for {chapter_name}:\n"
-        f"{raw_data}\n"
-        f"Difficulty: {difficulty}\n"
-        "Task: Create a 3-question quiz using the formulas found in the text."
-    )
-
-if __name__ == "__main__":
-    mcp.run(transport="stdio")
+from mcp.server.fastmcp import FastMCP
+import logging
+import os
+
+# Initialize the server
+mcp = FastMCP("Math-Education-Server")
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Path to your collected resource
+MARKDOWN_FILE = r"resource\jemh114-min (1).md" 
+
+def get_local_resource():
+    """Helper to read the markdown file safely."""
+    if os.path.exists(MARKDOWN_FILE):
+        with open(MARKDOWN_FILE, "r", encoding="utf-8") as f:
+            return f.read()
+    return "Resource file not found."
+
+# --- Task 1: Generate a Summary ---
+@mcp.tool()
+async def generate_chapter_summary(chapter_name: str) -> str:
+    """
+    Provides the source material for a chapter. 
+    The AI should use this to create a summary.
+    """
+    raw_data = get_local_resource()
+    
+    # We use XML-style tags. Senior engineers do this because 
+    # LLMs (especially Claude) are trained to handle tagged data perfectly.
+    return f"""
+    <source_material>
+    {raw_data[:4000]} 
+    </source_material>
+
+    SYSTEM_INSTRUCTION: You are a Mathematics Expert. 
+    1. Read the text inside <source_material>.
+    2. Create a summary with 3 bullet points.
+    3. List all formulas found in LaTeX format.
+    4. Do not repeat the raw text; only provide the summary.
+    """
+
+# --- Task 2: Quiz Generator ---
+@mcp.tool()
+async def generate_quiz(chapter_name: str, difficulty: str = "medium") -> str:
+    """
+    Extracts formulas from the markdown and asks the LLM to build a quiz.
+    """
+    raw_data = get_local_resource()
+    
+    # Using the LLM's ability to pick out formulas from the raw text provided
+    return (
+        f"Here is the raw material for {chapter_name}:\n"
+        f"{raw_data}\n"
+        f"Difficulty: {difficulty}\n"
+        "Task: Create a 3-question quiz using the formulas found in the text."
+    )
+
+if __name__ == "__main__":
+    # FastMCP handles the --transport sse argument automatically 
+    # if you use mcp.run()
+    mcp.run()