initial mcp server setup

intro_test/openai_test/server.py

@@ -0,0 +1,53 @@
+import os
+import json
+from mcp.server.fastmcp import FastMCP
+
+# Create an MCP server
+mcp = FastMCP(
+    name="Knowledge Base",
+    host="0.0.0.0",  # only used for SSE transport (localhost)
+    port=8050,  # only used for SSE transport (set this to any port)
+)
+
+
+@mcp.tool()
+def get_knowledge_base() -> str:
+    """Retrieve the entire knowledge base as a formatted string.
+
+    Returns:
+        A formatted string containing all Q&A pairs from the knowledge base.
+    """
+    try:
+        kb_path = os.path.join(os.path.dirname(__file__), "data", "kb.json")
+        with open(kb_path, "r") as f:
+            kb_data = json.load(f)
+
+        # Format the knowledge base as a string
+        kb_text = "Here is the retrieved knowledge base:\n\n"
+
+        if isinstance(kb_data, list):
+            for i, item in enumerate(kb_data, 1):
+                if isinstance(item, dict):
+                    question = item.get("question", "Unknown question")
+                    answer = item.get("answer", "Unknown answer")
+                else:
+                    question = f"Item {i}"
+                    answer = str(item)
+
+                kb_text += f"Q{i}: {question}\n"
+                kb_text += f"A{i}: {answer}\n\n"
+        else:
+            kb_text += f"Knowledge base content: {json.dumps(kb_data, indent=2)}\n\n"
+
+        return kb_text
+    except FileNotFoundError:
+        return "Error: Knowledge base file not found"
+    except json.JSONDecodeError:
+        return "Error: Invalid JSON in knowledge base file"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Run the server
+if __name__ == "__main__":
+    mcp.run(transport="stdio")