#!/usr/bin/env python3
"""
MCP server for: Create an MCP that monitors system resources (CPU, memory, disk, network), scrapes cryptocurrency pr...
Generated by KEN-MCP on 2025-06-26

TODO: Claude, please customize this MCP server based on these requirements:
Create an MCP that monitors system resources (CPU, memory, disk, network), 
        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, 
        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API 
        endpoints for historical data, WebSocket streaming for live updates, authentication with 
        JWT tokens, rate limiting per user, and interactive data visualization dashboards. 
        Include support for custom alert...

Instructions:
1. Rename the placeholder tools to match the actual functionality needed
2. Update tool descriptions and parameters based on requirements
3. Implement the actual logic in each tool function
4. Add/remove tools, resources, and prompts as needed
5. Update dependencies in pyproject.toml if additional packages are required
"""

from fastmcp import FastMCP, Context
from fastmcp.exceptions import ToolError
from typing import Dict, List, Any, Optional
from pathlib import Path
import json
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize the MCP server
mcp = FastMCP(
    name="system-monitor-mcp",
    instructions="""
    MCP server for: Create an MCP that monitors system resources (CPU, memory, disk, network), scrapes cryptocurrency pr...
    
    Original Requirements:
    Create an MCP that monitors system resources (CPU, memory, disk, network), 
        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, 
        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API 
        endpoints for historical data, WebSocket streaming for live updates, authentication with 
        JWT tokens, rate limiting per user, and interactive data visualization dashboards. 
        Include support for custom alert...
    
    TODO: Claude should update these instructions based on the actual implementation.
    """
)


@mcp.tool
async def start_monitor(
    ctx: Context,
    input_data: str,
    options: Optional[Dict[str, Any]]
) -> Dict[str, Any]:
    """Primary tool - TODO: Implement based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), 
        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, 
        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API 
        endpoints for historical data, WebSocket streaming for live updates, authentication with 
        JWT tokens, rate limiting per user, and interactive data visualization..."""
    # TODO: Claude, implement this tool based on the requirements in the docstring above
    # Consider:
    # - What data sources or APIs might be needed
    # - What processing or transformations are required
    # - What error cases should be handled
    # - What progress updates would be helpful
    
    try:
        # FastMCP Context Methods Reference:
        # - await ctx.info("message") - Log information
        # - await ctx.report_progress(current, total, "message") - Show progress
        # - await ctx.read_resource("uri") - Read from a resource
        
        await ctx.info(f"Starting start_monitor...")
        
        # TODO: Add parameter validation
        # Example patterns:
        # if not input_data:
        #     raise ToolError("input_data is required")
        # 
        # if not isinstance(input_data, str):
        #     raise ToolError("input_data must be a string")
        #
        # if len(input_data) > 1000:
        #     raise ToolError("input_data too long (max 1000 chars)")
        
        # TODO: Implement the main functionality
        # Common patterns by use case:
        #
        # For data storage:
        #   storage_dir = Path.home() / ".mcp_data" / "start_monitor"
        #   storage_dir.mkdir(parents=True, exist_ok=True)
        #
        # For API calls:
        #   import httpx
        #   async with httpx.AsyncClient() as client:
        #       response = await client.get(url)
        #
        # For file processing:
        #   from pathlib import Path
        #   file = Path(file_path)
        #   if not file.exists():
        #       raise ToolError(f"File not found: {file_path}")
        
        # TODO: Add progress updates for long operations
        # await ctx.report_progress(25, 100, "Loading data...")
        # await ctx.report_progress(50, 100, "Processing...")
        # await ctx.report_progress(75, 100, "Finalizing...")
        
        # TODO: Return appropriate result
        # Success pattern:
        # return {
        #     "success": True,
        #     "data": processed_data,
        #     "count": len(results),
        #     "message": "Operation completed successfully"
        # }
        
        result = {
            "status": "not_implemented",
            "message": f"TODO: Implement start_monitor",
            "tool": "start_monitor",
            "description": "Primary tool - TODO: Implement based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), \n        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, \n        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API \n        endpoints for historical data, WebSocket streaming for live updates, authentication with \n        JWT tokens, rate limiting per user, and interactive data visualization dashboards. \n        Include support for custom alert rules, data export in multiple formats, and integration \n        with monitoring services like Datadog and PagerDuty.",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"start_monitor completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"start_monitor error: {str(e)}")


@mcp.tool
async def list_items(
    ctx: Context,
    param1: str,
    param2: Optional[int]
) -> Dict[str, Any]:
    """Secondary tool - TODO: Implement based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), 
        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, 
        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API 
        endpoints for historical data, WebSocket streaming for live updates, authentication with 
        JWT tokens, rate limiting per user, and interactive data visualizati..."""
    # TODO: Claude, implement this tool based on the requirements in the docstring above
    # Consider:
    # - What data sources or APIs might be needed
    # - What processing or transformations are required
    # - What error cases should be handled
    # - What progress updates would be helpful
    
    try:
        # FastMCP Context Methods Reference:
        # - await ctx.info("message") - Log information
        # - await ctx.report_progress(current, total, "message") - Show progress
        # - await ctx.read_resource("uri") - Read from a resource
        
        await ctx.info(f"Starting list_items...")
        
        # TODO: Add parameter validation
        # Example patterns:
        # if not input_data:
        #     raise ToolError("input_data is required")
        # 
        # if not isinstance(input_data, str):
        #     raise ToolError("input_data must be a string")
        #
        # if len(input_data) > 1000:
        #     raise ToolError("input_data too long (max 1000 chars)")
        
        # TODO: Implement the main functionality
        # Common patterns by use case:
        #
        # For data storage:
        #   storage_dir = Path.home() / ".mcp_data" / "list_items"
        #   storage_dir.mkdir(parents=True, exist_ok=True)
        #
        # For API calls:
        #   import httpx
        #   async with httpx.AsyncClient() as client:
        #       response = await client.get(url)
        #
        # For file processing:
        #   from pathlib import Path
        #   file = Path(file_path)
        #   if not file.exists():
        #       raise ToolError(f"File not found: {file_path}")
        
        # TODO: Add progress updates for long operations
        # await ctx.report_progress(25, 100, "Loading data...")
        # await ctx.report_progress(50, 100, "Processing...")
        # await ctx.report_progress(75, 100, "Finalizing...")
        
        # TODO: Return appropriate result
        # Success pattern:
        # return {
        #     "success": True,
        #     "data": processed_data,
        #     "count": len(results),
        #     "message": "Operation completed successfully"
        # }
        
        result = {
            "status": "not_implemented",
            "message": f"TODO: Implement list_items",
            "tool": "list_items",
            "description": "Secondary tool - TODO: Implement based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), \n        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, \n        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API \n        endpoints for historical data, WebSocket streaming for live updates, authentication with \n        JWT tokens, rate limiting per user, and interactive data visualization dashboards. \n        Include support for custom alert rules, data export in multiple formats, and integration \n        with monitoring services like Datadog and PagerDuty.",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"list_items completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"list_items error: {str(e)}")


@mcp.tool
async def update_item(
    ctx: Context,
    data: Any
) -> Dict[str, Any]:
    """Additional tool - TODO: Implement or remove based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), 
        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, 
        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API 
        endpoints for historical data, WebSocket streaming for live updates, authentication with 
        JWT tokens, rate limiting per user, and interactive data ..."""
    # TODO: Claude, implement this tool based on the requirements in the docstring above
    # Consider:
    # - What data sources or APIs might be needed
    # - What processing or transformations are required
    # - What error cases should be handled
    # - What progress updates would be helpful
    
    try:
        # FastMCP Context Methods Reference:
        # - await ctx.info("message") - Log information
        # - await ctx.report_progress(current, total, "message") - Show progress
        # - await ctx.read_resource("uri") - Read from a resource
        
        await ctx.info(f"Starting update_item...")
        
        # TODO: Add parameter validation
        # Example patterns:
        # if not input_data:
        #     raise ToolError("input_data is required")
        # 
        # if not isinstance(input_data, str):
        #     raise ToolError("input_data must be a string")
        #
        # if len(input_data) > 1000:
        #     raise ToolError("input_data too long (max 1000 chars)")
        
        # TODO: Implement the main functionality
        # Common patterns by use case:
        #
        # For data storage:
        #   storage_dir = Path.home() / ".mcp_data" / "update_item"
        #   storage_dir.mkdir(parents=True, exist_ok=True)
        #
        # For API calls:
        #   import httpx
        #   async with httpx.AsyncClient() as client:
        #       response = await client.get(url)
        #
        # For file processing:
        #   from pathlib import Path
        #   file = Path(file_path)
        #   if not file.exists():
        #       raise ToolError(f"File not found: {file_path}")
        
        # TODO: Add progress updates for long operations
        # await ctx.report_progress(25, 100, "Loading data...")
        # await ctx.report_progress(50, 100, "Processing...")
        # await ctx.report_progress(75, 100, "Finalizing...")
        
        # TODO: Return appropriate result
        # Success pattern:
        # return {
        #     "success": True,
        #     "data": processed_data,
        #     "count": len(results),
        #     "message": "Operation completed successfully"
        # }
        
        result = {
            "status": "not_implemented",
            "message": f"TODO: Implement update_item",
            "tool": "update_item",
            "description": "Additional tool - TODO: Implement or remove based on requirements: Create an MCP that monitors system resources (CPU, memory, disk, network), \n        scrapes cryptocurrency prices from multiple APIs, stores time-series data in SQLite, \n        generates real-time alerts via webhooks when thresholds are exceeded, provides REST API \n        endpoints for historical data, WebSocket streaming for live updates, authentication with \n        JWT tokens, rate limiting per user, and interactive data visualization dashboards. \n        Include support for custom alert rules, data export in multiple formats, and integration \n        with monitoring services like Datadog and PagerDuty.",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"update_item completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"update_item error: {str(e)}")


# Resources - TODO: Claude, implement these based on requirements

@mcp.resource("data://items")
async def resource_items() -> List[Dict[str, Any]]:
    """Collection of items - TODO: Implement based on requirements"""
    # TODO: Implement this resource based on requirements
    # Consider what data should be exposed here
    return [{
        "status": "not_implemented", 
        "message": "TODO: Implement resource for data://items",
        "description": "Collection of items - TODO: Implement based on requirements"
    }]

# Prompts - TODO: Claude, implement these based on requirements

@mcp.prompt
def help(topic: Optional[str]) -> str:
    """TODO: Generate contextual help based on requirements"""
    # TODO: Implement this prompt based on requirements
    # Return a string that will be converted to a user message
    # or return a PromptMessage object for more control
    return f"TODO: Implement help prompt - {locals()}"

@mcp.prompt
def assistant(query: str) -> str:
    """Interactive assistant for guidance - TODO: Customize based on requirements"""
    # TODO: Implement this prompt based on requirements
    # Return a string that will be converted to a user message
    # or return a PromptMessage object for more control
    return f"TODO: Implement assistant prompt - {locals()}"


if __name__ == "__main__":
    mcp.run()
