Update mcp_server.py

mcp_server.py

@@ -1,114 +1,3 @@
-# from mcp.server.fastmcp import FastMCP
-# import json
-# from typing import Dict, List, Any
-# import logging
-
-# # Set up logging
-# logging.basicConfig(level=logging.INFO)
-# logger = logging.getLogger(__name__)
-
-# # Initialize MCP server
-# mcp = FastMCP("intelligent-content-organizer")
-
-# @mcp.tool()
-# async def process_file(file_path: str) -> Dict[str, Any]:
-#     """
-#     Process a local file and extract content, generate tags, and create embeddings
-    
-#     Args:
-#         file_path: Path to the file to process
-        
-#     Returns:
-#         Dictionary containing processed content, tags, and metadata
-#     """
-#     try:
-#         from mcp_tools import process_local_file
-#         result = await process_local_file(file_path)
-#         return result
-#     except Exception as e:
-#         logger.error(f"Error processing file: {str(e)}")
-#         return {"error": str(e)}
-
-# @mcp.tool()
-# async def process_url(url: str) -> Dict[str, Any]:
-#     """
-#     Fetch and process content from a URL
-    
-#     Args:
-#         url: URL to fetch and process
-        
-#     Returns:
-#         Dictionary containing processed content, tags, and metadata
-#     """
-#     try:
-#         from mcp_tools import process_web_content
-#         result = await process_web_content(url)
-#         return result
-#     except Exception as e:
-#         logger.error(f"Error processing URL: {str(e)}")
-#         return {"error": str(e)}
-
-# @mcp.tool()
-# async def semantic_search(query: str, limit: int = 5) -> List[Dict[str, Any]]:
-#     """
-#     Perform semantic search across stored documents
-    
-#     Args:
-#         query: Search query
-#         limit: Maximum number of results to return
-        
-#     Returns:
-#         List of relevant documents with metadata
-#     """
-#     try:
-#         from mcp_tools import search_knowledge_base
-#         results = await search_knowledge_base(query, limit)
-#         return results
-#     except Exception as e:
-#         logger.error(f"Error performing search: {str(e)}")
-#         return [{"error": str(e)}]
-
-# @mcp.tool()
-# async def get_document_summary(doc_id: str) -> Dict[str, Any]:
-#     """
-#     Get summary and metadata for a specific document
-    
-#     Args:
-#         doc_id: Document ID in the knowledge base
-        
-#     Returns:
-#         Document summary and metadata
-#     """
-#     try:
-#         from mcp_tools import get_document_details
-#         result = await get_document_details(doc_id)
-#         return result
-#     except Exception as e:
-#         logger.error(f"Error getting document summary: {str(e)}")
-#         return {"error": str(e)}
-
-# # Server metadata
-# @mcp.resource("server_info")
-# async def get_server_info() -> Dict[str, Any]:
-#     """Get information about this MCP server"""
-#     return {
-#         "name": "Intelligent Content Organizer",
-#         "version": "1.0.0",
-#         "description": "AI-powered knowledge management system with automatic tagging and semantic search",
-#         "capabilities": [
-#             "File processing (20+ formats)",
-#             "Web content extraction",
-#             "Automatic tagging",
-#             "Semantic search",
-#             "Document summarization"
-#         ]
-#     }
-
-# if __name__ == "__main__":
-#     # Run the MCP server
-#     import asyncio
-#     asyncio.run(mcp.run())
-
 from mcp.server.fastmcp import FastMCP
 import json
 from typing import Dict, List, Any