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# 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
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
"""
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
"""
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
"""
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
"""
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)}
@mcp.tool()
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"
],
"tools": [
{
"name": "process_file",
"description": "Process local files and extract content"
},
{
"name": "process_url",
"description": "Fetch and process web content"
},
{
"name": "semantic_search",
"description": "Search across stored documents"
},
{
"name": "get_document_summary",
"description": "Get document details"
},
{
"name": "get_server_info",
"description": "Get server information"
}
]
}
if __name__ == "__main__":
# Run the MCP server
import asyncio
asyncio.run(mcp.run())
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