#!/usr/bin/env python3
"""
MCP server for: Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file p...
Generated by KEN-MCP on 2025-06-25

TODO: Claude, please customize this MCP server based on these requirements:
Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like "résumé_中文_🚀.pdf" and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/测试/文件夹/and/emoji/🚀/in/paths

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

# Initialize the MCP server
mcp = FastMCP(
    name="unicode-file-processor",
    instructions="""
    MCP server for: Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file p...
    
    Original Requirements:
    Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like "résumé_中文_🚀.pdf" and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/测试/文件夹/and/emoji/🚀/in/paths
    
    TODO: Claude should update these instructions based on the actual implementation.
    """
)


@mcp.tool
async def tool_one(
    ctx: Context,
    input_data: str,
    options: Optional[Dict[str, Any]]
) -> Dict[str, Any]:
    """Primary tool - TODO: Implement based on requirements: Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like 'résumé_中文_🚀.pdf' and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/测试/文件夹/and/emoji/🚀/in/paths"""
    # 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 tool_one...")
        
        # 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" / "tool_one"
        #   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 tool_one",
            "tool": "tool_one",
            "description": "Primary tool - TODO: Implement based on requirements: Build an MCP that processes files with unicode characters (\u00f1, \u4e2d\u6587, emoji \ud83d\ude80), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like \"r\u00e9sum\u00e9_\u4e2d\u6587_\ud83d\ude80.pdf\" and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/\u6d4b\u8bd5/\u6587\u4ef6\u5939/and/emoji/\ud83d\ude80/in/paths",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"tool_one completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"tool_one error: {str(e)}")


@mcp.tool
async def tool_two(
    ctx: Context,
    param1: str,
    param2: Optional[int]
) -> Dict[str, Any]:
    """Secondary tool - TODO: Implement based on requirements: Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like 'résumé_中文_🚀.pdf' and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/测试/文件夹/and/emoji/🚀/in/paths"""
    # 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 tool_two...")
        
        # 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" / "tool_two"
        #   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 tool_two",
            "tool": "tool_two",
            "description": "Secondary tool - TODO: Implement based on requirements: Build an MCP that processes files with unicode characters (\u00f1, \u4e2d\u6587, emoji \ud83d\ude80), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like \"r\u00e9sum\u00e9_\u4e2d\u6587_\ud83d\ude80.pdf\" and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/\u6d4b\u8bd5/\u6587\u4ef6\u5939/and/emoji/\ud83d\ude80/in/paths",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"tool_two completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"tool_two error: {str(e)}")


@mcp.tool
async def tool_three(
    ctx: Context,
    data: Any
) -> Dict[str, Any]:
    """Additional tool - TODO: Implement or remove based on requirements: Build an MCP that processes files with unicode characters (ñ, 中文, emoji 🚀), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like 'résumé_中文_🚀.pdf' and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/测试/文件夹/and/emoji/🚀/in..."""
    # 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 tool_three...")
        
        # 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" / "tool_three"
        #   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 tool_three",
            "tool": "tool_three",
            "description": "Additional tool - TODO: Implement or remove based on requirements: Build an MCP that processes files with unicode characters (\u00f1, \u4e2d\u6587, emoji \ud83d\ude80), handles very long file paths (>255 chars), manages file permissions, creates nested directories, and performs batch operations on thousands of files. Support CSV, JSON, XML, PDF, and image formats. Process files like \"r\u00e9sum\u00e9_\u4e2d\u6587_\ud83d\ude80.pdf\" and paths like /very/long/path/that/exceeds/normal/filesystem/limits/with/unicode/characters/like/\u6d4b\u8bd5/\u6587\u4ef6\u5939/and/emoji/\ud83d\ude80/in/paths",
            "input": locals()  # Shows all parameters for debugging
        }
        
        await ctx.info(f"tool_three completed")
        return result
        
    except Exception as e:
        # Always use ToolError for user-facing errors
        raise ToolError(f"tool_three error: {str(e)}")


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

@mcp.resource("data://items")
async def resource_items() -> List[Dict[str, Any]]:
    """TODO: List of items - 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": "TODO: List of items - implement based on requirements"
    }]

@mcp.resource("resource://config")
async def resource_config() -> List[Dict[str, Any]]:
    """TODO: Configuration data - 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 resource://config",
        "description": "TODO: Configuration data - implement based on requirements"
    }]

@mcp.resource("data://status")
async def resource_status() -> List[Dict[str, Any]]:
    """TODO: Status information - implement or remove 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://status",
        "description": "TODO: Status information - implement or remove 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:
    """TODO: Assistant prompt - 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()
