"""
Document MCP Server

This module provides MCP server functionality for document processing and analysis.
It handles various document formats including:
- Text files
- PDF documents
- Word documents (DOCX)
- Excel spreadsheets
- PowerPoint presentations
- JSON and XML files
- Source code files

Each document type has specialized processing functions that extract content,
structure, and metadata. The server focuses on local file processing with
appropriate validation and error handling.

Main functions:
- mcpreadtext: Reads plain text files
- mcpreadpdf: Reads PDF files with optional image extraction
- mcpreaddocx: Reads Word documents
- mcpreadexcel: Reads Excel spreadsheets
- mcpreadpptx: Reads PowerPoint presentations
- mcpreadjson: Reads and parses JSON/JSONL files
- mcpreadxml: Reads and parses XML files
- mcpreadsourcecode: Reads and analyzes source code files
"""

import io
import json
import os
import sys
import tempfile
import traceback
from datetime import date, datetime
from typing import Any, Dict, List, Optional

import fitz
import html2text
import pandas as pd
import xmltodict
from bs4 import BeautifulSoup
from docx2markdown._docx_to_markdown import docx_to_markdown
from dotenv import load_dotenv
from mcp.server.fastmcp import FastMCP
from PIL import Image, ImageDraw, ImageFont
from pptx import Presentation
from pydantic import BaseModel, Field
from PyPDF2 import PdfReader
from tabulate import tabulate
from xls2xlsx import XLS2XLSX

from aworld.logs.util import logger
from aworld.utils import import_package
from mcp_servers.image_server import encode_images

mcp = FastMCP("document-server")


# Define model classes for different document types
class TextDocument(BaseModel):
    """Model representing a text document"""

    content: str
    file_path: str
    file_name: str
    file_size: int
    last_modified: str


class HtmlDocument(BaseModel):
    """Model representing an HTML document"""

    content: str  # Extracted text content
    html_content: str  # Original HTML content
    file_path: str
    file_name: str
    file_size: int
    last_modified: str
    title: Optional[str] = None
    links: Optional[List[Dict[str, str]]] = None
    images: Optional[List[Dict[str, str]]] = None
    tables: Optional[List[str]] = None
    markdown: Optional[str] = None  # HTML converted to Markdown format


class JsonDocument(BaseModel):
    """Model representing a JSON document"""

    format: str  # "json" or "jsonl"
    type: Optional[str] = None  # "array" or "object" for standard JSON
    count: Optional[int] = None
    keys: Optional[List[str]] = None
    data: Any
    file_path: str
    file_name: str


class XmlDocument(BaseModel):
    """Model representing an XML document"""

    content: Dict
    file_path: str
    file_name: str


class PdfImage(BaseModel):
    """Model representing an image extracted from a PDF"""

    page: int
    format: str
    width: int
    height: int
    path: str


class PdfDocument(BaseModel):
    """Model representing a PDF document"""

    content: str
    file_path: str
    file_name: str
    page_count: int
    images: Optional[List[PdfImage]] = None
    image_count: Optional[int] = None
    image_dir: Optional[str] = None
    error: Optional[str] = None


class PdfResult(BaseModel):
    """Model representing results from processing multiple PDF documents"""

    total_files: int
    success_count: int
    failed_count: int
    results: List[PdfDocument]


class DocxDocument(BaseModel):
    """Model representing a Word document"""

    content: str
    file_path: str
    file_name: str


class ExcelSheet(BaseModel):
    """Model representing a sheet in an Excel file"""

    name: str
    data: List[Dict[str, Any]]
    markdown_table: str
    row_count: int
    column_count: int


class ExcelDocument(BaseModel):
    """Model representing an Excel document"""

    file_name: str
    file_path: str
    processed_path: Optional[str] = None
    file_type: str
    sheet_count: int
    sheet_names: List[str]
    sheets: List[ExcelSheet]
    success: bool = True
    error: Optional[str] = None


class ExcelResult(BaseModel):
    """Model representing results from processing multiple Excel documents"""

    total_files: int
    success_count: int
    failed_count: int
    results: List[ExcelDocument]


class PowerPointSlide(BaseModel):
    """Model representing a slide in a PowerPoint presentation"""

    slide_number: int
    image: str  # Base64 encoded image


class PowerPointDocument(BaseModel):
    """Model representing a PowerPoint document"""

    file_path: str
    file_name: str
    slide_count: int
    slides: List[PowerPointSlide]


class SourceCodeDocument(BaseModel):
    """Model representing a source code document"""

    content: str
    file_type: str
    file_path: str
    file_name: str
    line_count: int
    size_bytes: int
    last_modified: str
    classes: Optional[List[str]] = None
    functions: Optional[List[str]] = None
    imports: Optional[List[str]] = None
    package: Optional[List[str]] = None
    methods: Optional[List[str]] = None
    includes: Optional[List[str]] = None


class DocumentError(BaseModel):
    """Model representing an error in document processing"""

    error: str
    file_path: Optional[str] = None
    file_name: Optional[str] = None


class ComplexEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, datetime):
            return o.strftime("%Y-%m-%d %H:%M:%S")
        elif isinstance(o, date):
            return o.strftime("%Y-%m-%d")
        else:
            return json.JSONEncoder.default(self, o)


def handle_error(e: Exception, error_type: str, file_path: Optional[str] = None) -> str:
    """Unified error handling and return standard format error message"""
    error_msg = f"{error_type} error: {str(e)}"
    logger.error(traceback.format_exc())

    error = DocumentError(
        error=error_msg,
        file_path=file_path,
        file_name=os.path.basename(file_path) if file_path else None,
    )

    return error.model_dump_json()


def check_file_readable(document_path: str) -> str:
    """Check if file exists and is readable, return error message or None"""
    if not os.path.exists(document_path):
        return f"File does not exist: {document_path}"
    if not os.access(document_path, os.R_OK):
        return f"File is not readable: {document_path}"
    return None


@mcp.tool(
    description="Read and return content from local text file. Cannot process https://URLs files."
)
def mcpreadtext(
    document_path: str = Field(description="The input local text file path."),
) -> str:
    """Read and return content from local text file. Cannot process https://URLs files."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    try:
        with open(document_path, "r", encoding="utf-8") as f:
            content = f.read()

        result = TextDocument(
            content=content,
            file_path=document_path,
            file_name=os.path.basename(document_path),
            file_size=os.path.getsize(document_path),
            last_modified=datetime.fromtimestamp(
                os.path.getmtime(document_path)
            ).strftime("%Y-%m-%d %H:%M:%S"),
        )

        return result.model_dump_json()
    except Exception as e:
        return handle_error(e, "Text file reading", document_path)


@mcp.tool(
    description="Read and parse JSON or JSONL file, return the parsed content. Cannot process https://URLs files."
)
def mcpreadjson(
    document_path: str = Field(description="Local path to JSON or JSONL file"),
    is_jsonl: bool = Field(
        default=False,
        description="Whether the file is in JSONL format (one JSON object per line)",
    ),
) -> str:
    """Read and parse JSON or JSONL file, return the parsed content. Cannot process https://URLs files."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    try:
        # Choose processing method based on file type
        if is_jsonl:
            # Process JSONL file (one JSON object per line)
            results = []
            with open(document_path, "r", encoding="utf-8") as f:
                for line_num, line in enumerate(f, 1):
                    line = line.strip()
                    if not line:
                        continue
                    try:
                        json_obj = json.loads(line)
                        results.append(json_obj)
                    except json.JSONDecodeError as e:
                        logger.warning(
                            f"JSON parsing error at line {line_num}: {str(e)}"
                        )

            # Create result model
            result = JsonDocument(
                format="jsonl",
                count=len(results),
                data=results,
                file_path=document_path,
                file_name=os.path.basename(document_path),
            )

        else:
            # Process standard JSON file
            with open(document_path, "r", encoding="utf-8") as f:
                data = json.load(f)

            # Create result model based on data type
            if isinstance(data, list):
                result = JsonDocument(
                    format="json",
                    type="array",
                    count=len(data),
                    data=data,
                    file_path=document_path,
                    file_name=os.path.basename(document_path),
                )
            else:
                result = JsonDocument(
                    format="json",
                    type="object",
                    keys=list(data.keys()) if isinstance(data, dict) else [],
                    data=data,
                    file_path=document_path,
                    file_name=os.path.basename(document_path),
                )

        return result.model_dump_json()

    except json.JSONDecodeError as e:
        return handle_error(e, "JSON parsing", document_path)
    except Exception as e:
        return handle_error(e, "JSON file reading", document_path)


@mcp.tool(
    description="Read and return content from XML file. return the parsed content. Cannot process https://URLs files."
)
def mcpreadxml(
    document_path: str = Field(description="The local input XML file path."),
) -> str:
    """Read and return content from XML file. Cannot process https://URLs files."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    try:
        with open(document_path, "r", encoding="utf-8") as f:
            data = f.read()

        result = XmlDocument(
            content=xmltodict.parse(data),
            file_path=document_path,
            file_name=os.path.basename(document_path),
        )

        return result.model_dump_json()
    except Exception as e:
        return handle_error(e, "XML file reading", document_path)


@mcp.tool(
    description="Read and return content from PDF file with optional image extraction. return the parsed content. Cannot process https://URLs files."
)
def mcpreadpdf(
    document_paths: List[str] = Field(description="The local input PDF file paths."),
    extract_images: bool = Field(
        default=False, description="Whether to extract images from PDF (default: False)"
    ),
) -> str:
    """Read and return content from PDF file with optional image extraction. Cannot process https://URLs files."""
    try:

        results = []
        success_count = 0
        failed_count = 0

        for document_path in document_paths:
            error = check_file_readable(document_path)
            if error:
                results.append(
                    PdfDocument(
                        content="",
                        file_path=document_path,
                        file_name=os.path.basename(document_path),
                        page_count=0,
                        error=error,
                    )
                )
                failed_count += 1
                continue

            try:
                with open(document_path, "rb") as f:
                    reader = PdfReader(f)
                    content = " ".join(page.extract_text() for page in reader.pages)
                    page_count = len(reader.pages)

                    pdf_result = PdfDocument(
                        content=content,
                        file_path=document_path,
                        file_name=os.path.basename(document_path),
                        page_count=page_count,
                    )

                    # Extract images if requested
                    if extract_images:
                        images_data = []
                        # Use /tmp directory for storing images
                        output_dir = "/tmp/pdf_images"

                        # Create output directory if it doesn't exist
                        os.makedirs(output_dir, exist_ok=True)

                        # Generate a unique subfolder based on filename to avoid conflicts
                        pdf_name = os.path.splitext(os.path.basename(document_path))[0]
                        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
                        image_dir = os.path.join(output_dir, f"{pdf_name}_{timestamp}")
                        os.makedirs(image_dir, exist_ok=True)

                        try:
                            # Open PDF with PyMuPDF
                            pdf_document = fitz.open(document_path)

                            # Iterate through each page
                            for page_index in range(len(pdf_document)):
                                page = pdf_document[page_index]

                                # Get image list
                                image_list = page.get_images(full=True)

                                # Process each image
                                for img_index, img in enumerate(image_list):
                                    # Extract image information
                                    xref = img[0]
                                    base_image = pdf_document.extract_image(xref)
                                    image_bytes = base_image["image"]
                                    image_ext = base_image["ext"]

                                    # Save image to file in /tmp directory
                                    img_filename = f"pdf_image_p{page_index+1}_{img_index+1}.{image_ext}"
                                    img_path = os.path.join(image_dir, img_filename)

                                    with open(img_path, "wb") as img_file:
                                        img_file.write(image_bytes)
                                        logger.success(f"Image saved: {img_path}")

                                    # Get image dimensions
                                    with Image.open(img_path) as img:
                                        width, height = img.size

                                    # Add to results with file path instead of base64
                                    images_data.append(
                                        PdfImage(
                                            page=page_index + 1,
                                            format=image_ext,
                                            width=width,
                                            height=height,
                                            path=img_path,
                                        )
                                    )

                            pdf_result.images = images_data
                            pdf_result.image_count = len(images_data)
                            pdf_result.image_dir = image_dir

                        except Exception as img_error:
                            logger.error(f"Error extracting images: {str(img_error)}")
                            # Don't clean up on error so we can keep any successfully extracted images
                            pdf_result.error = str(img_error)

                results.append(pdf_result)
                success_count += 1

            except Exception as e:
                results.append(
                    PdfDocument(
                        content="",
                        file_path=document_path,
                        file_name=os.path.basename(document_path),
                        page_count=0,
                        error=str(e),
                    )
                )
                failed_count += 1

        # Create final result
        pdf_result = PdfResult(
            total_files=len(document_paths),
            success_count=success_count,
            failed_count=failed_count,
            results=results,
        )

        return pdf_result.model_dump_json()

    except Exception as e:
        return handle_error(e, "PDF file reading")


@mcp.tool(
    description="Read and return content from Word file. return the parsed content. Cannot process https://URLs files."
)
def mcpreaddocx(
    document_path: str = Field(description="The local input Word file path."),
) -> str:
    """Read and return content from Word file. Cannot process https://URLs files."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    try:

        file_name = os.path.basename(document_path)
        md_file_path = f"{file_name}.md"
        docx_to_markdown(document_path, md_file_path)

        with open(md_file_path, "r", encoding="utf-8") as f:
            content = f.read()

        os.remove(md_file_path)

        result = DocxDocument(
            content=content, file_path=document_path, file_name=file_name
        )

        return result.model_dump_json()
    except Exception as e:
        return handle_error(e, "Word file reading", document_path)


@mcp.tool(
    description="Read multiple Excel/CSV files and convert sheets to Markdown tables. return the parsed content. Cannot process https://URLs files."
)
def mcpreadexcel(
    document_paths: List[str] = Field(
        description="List of local input Excel/CSV file paths."
    ),
    max_rows: int = Field(
        1000, description="Maximum number of rows to read per sheet (default: 1000)"
    ),
    convert_xls_to_xlsx: bool = Field(
        False,
        description="Whether to convert XLS files to XLSX format (default: False)",
    ),
) -> str:
    """Read multiple Excel/CSV files and convert sheets to Markdown tables. Cannot process https://URLs files."""
    try:

        # Import required packages
        import_package("tabulate")

        # Import xls2xlsx package if conversion is requested
        if convert_xls_to_xlsx:
            import_package("xls2xlsx")

        all_results = []
        temp_files = []  # Track temporary files for cleanup
        success_count = 0
        failed_count = 0

        # Process each file
        for document_path in document_paths:
            # Check if file exists and is readable
            error = check_file_readable(document_path)
            if error:
                all_results.append(
                    ExcelDocument(
                        file_name=os.path.basename(document_path),
                        file_path=document_path,
                        file_type="UNKNOWN",
                        sheet_count=0,
                        sheet_names=[],
                        sheets=[],
                        success=False,
                        error=error,
                    )
                )
                failed_count += 1
                continue

            try:
                # Check file extension
                file_ext = os.path.splitext(document_path)[1].lower()

                # Validate file type
                if file_ext not in [".csv", ".xls", ".xlsx", ".xlsm"]:
                    error_msg = f"Unsupported file format: {file_ext}. Only CSV, XLS, XLSX, and XLSM formats are supported."
                    all_results.append(
                        ExcelDocument(
                            file_name=os.path.basename(document_path),
                            file_path=document_path,
                            file_type=file_ext.replace(".", "").upper(),
                            sheet_count=0,
                            sheet_names=[],
                            sheets=[],
                            success=False,
                            error=error_msg,
                        )
                    )
                    failed_count += 1
                    continue

                # Convert XLS to XLSX if requested and file is XLS
                processed_path = document_path
                if convert_xls_to_xlsx and file_ext == ".xls":
                    try:
                        logger.info(f"Converting XLS to XLSX: {document_path}")
                        converter = XLS2XLSX(document_path)
                        # Create temp file with xlsx extension
                        xlsx_path = (
                            os.path.splitext(document_path)[0] + "_converted.xlsx"
                        )
                        converter.to_xlsx(xlsx_path)
                        processed_path = xlsx_path
                        temp_files.append(xlsx_path)  # Track for cleanup
                        logger.success(f"Converted XLS to XLSX: {xlsx_path}")
                    except Exception as conv_error:
                        logger.error(f"XLS to XLSX conversion error: {str(conv_error)}")
                        # Continue with original file if conversion fails

                excel_sheets = []
                sheet_names = []

                # Handle CSV files differently
                if file_ext == ".csv":
                    # For CSV files, create a single sheet with the file name
                    sheet_name = os.path.basename(document_path).replace(".csv", "")
                    df = pd.read_csv(processed_path, nrows=max_rows)

                    # Create markdown table
                    markdown_table = "*Empty table*"
                    if not df.empty:
                        headers = df.columns.tolist()
                        table_data = df.values.tolist()
                        markdown_table = tabulate(
                            table_data, headers=headers, tablefmt="pipe"
                        )

                        if len(df) >= max_rows:
                            markdown_table += (
                                f"\n\n*Note: Table truncated to {max_rows} rows*"
                            )

                    # Create sheet model
                    excel_sheets.append(
                        ExcelSheet(
                            name=sheet_name,
                            data=df.to_dict(orient="records"),
                            markdown_table=markdown_table,
                            row_count=len(df),
                            column_count=len(df.columns),
                        )
                    )

                    sheet_names = [sheet_name]

                else:
                    # For Excel files, process all sheets
                    with pd.ExcelFile(processed_path) as xls:
                        sheet_names = xls.sheet_names

                        for sheet_name in sheet_names:
                            # Read Excel sheet into DataFrame with row limit
                            df = pd.read_excel(
                                xls, sheet_name=sheet_name, nrows=max_rows
                            )

                            # Create markdown table
                            markdown_table = "*Empty table*"
                            if not df.empty:
                                headers = df.columns.tolist()
                                table_data = df.values.tolist()
                                markdown_table = tabulate(
                                    table_data, headers=headers, tablefmt="pipe"
                                )

                                if len(df) >= max_rows:
                                    markdown_table += f"\n\n*Note: Table truncated to {max_rows} rows*"

                            # Create sheet model
                            excel_sheets.append(
                                ExcelSheet(
                                    name=sheet_name,
                                    data=df.to_dict(orient="records"),
                                    markdown_table=markdown_table,
                                    row_count=len(df),
                                    column_count=len(df.columns),
                                )
                            )

                # Create result for this file
                file_result = ExcelDocument(
                    file_name=os.path.basename(document_path),
                    file_path=document_path,
                    processed_path=(
                        processed_path if processed_path != document_path else None
                    ),
                    file_type=file_ext.replace(".", "").upper(),
                    sheet_count=len(sheet_names),
                    sheet_names=sheet_names,
                    sheets=excel_sheets,
                    success=True,
                )

                all_results.append(file_result)
                success_count += 1

            except Exception as file_error:
                # Handle errors for individual files
                error_msg = str(file_error)
                logger.error(f"File reading error for {document_path}: {error_msg}")
                all_results.append(
                    ExcelDocument(
                        file_name=os.path.basename(document_path),
                        file_path=document_path,
                        file_type=os.path.splitext(document_path)[1]
                        .replace(".", "")
                        .upper(),
                        sheet_count=0,
                        sheet_names=[],
                        sheets=[],
                        success=False,
                        error=error_msg,
                    )
                )
                failed_count += 1

        # Clean up temporary files
        for temp_file in temp_files:
            try:
                if os.path.exists(temp_file):
                    os.remove(temp_file)
                    logger.info(f"Removed temporary file: {temp_file}")
            except Exception as cleanup_error:
                logger.warning(
                    f"Error cleaning up temporary file {temp_file}: {str(cleanup_error)}"
                )

        # Create final result
        excel_result = ExcelResult(
            total_files=len(document_paths),
            success_count=success_count,
            failed_count=failed_count,
            results=all_results,
        )

        return excel_result.model_dump_json()

    except Exception as e:
        return handle_error(e, "Excel/CSV files processing")


@mcp.tool(
    description="Read and convert PowerPoint slides to base64 encoded images. return the parsed content. Cannot process https://URLs files."
)
def mcpreadpptx(
    document_path: str = Field(description="The local input PowerPoint file path."),
) -> str:
    """Read and convert PowerPoint slides to base64 encoded images. Cannot process https://URLs files."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    # Create temporary directory
    temp_dir = tempfile.mkdtemp()
    slides_data = []

    try:
        presentation = Presentation(document_path)
        total_slides = len(presentation.slides)

        if total_slides == 0:
            raise ValueError("PPT file does not contain any slides")

        # Process each slide
        for i, slide in enumerate(presentation.slides):
            # Set slide dimensions
            slide_width_px = 1920  # 16:9 ratio
            slide_height_px = 1080

            # Create blank image
            slide_img = Image.new("RGB", (slide_width_px, slide_height_px), "white")
            draw = ImageDraw.Draw(slide_img)
            font = ImageFont.load_default()

            # Draw slide number
            draw.text((20, 20), f"Slide {i+1}/{total_slides}", fill="black", font=font)

            # Process shapes in the slide
            for shape in slide.shapes:
                try:
                    # Process images
                    if hasattr(shape, "image") and shape.image:
                        image_stream = io.BytesIO(shape.image.blob)
                        img = Image.open(image_stream)
                        left = int(
                            shape.left * slide_width_px / presentation.slide_width
                        )
                        top = int(
                            shape.top * slide_height_px / presentation.slide_height
                        )
                        slide_img.paste(img, (left, top))

                    # Process text
                    elif hasattr(shape, "text") and shape.text:
                        text_left = int(
                            shape.left * slide_width_px / presentation.slide_width
                        )
                        text_top = int(
                            shape.top * slide_height_px / presentation.slide_height
                        )
                        draw.text(
                            (text_left, text_top),
                            shape.text,
                            fill="black",
                            font=font,
                        )

                except Exception as shape_error:
                    logger.warning(
                        f"Error processing shape in slide {i+1}: {str(shape_error)}"
                    )

            # Save slide image
            img_path = os.path.join(temp_dir, f"slide_{i+1}.jpg")
            slide_img.save(img_path, "JPEG")

            # Convert to base64
            base64_image = encode_images(img_path)
            slides_data.append(
                PowerPointSlide(
                    slide_number=i + 1, image=f"data:image/jpeg;base64,{base64_image}"
                )
            )

        # Create result
        result = PowerPointDocument(
            file_path=document_path,
            file_name=os.path.basename(document_path),
            slide_count=total_slides,
            slides=slides_data,
        )

        return result.model_dump_json()

    except Exception as e:
        return handle_error(e, "PowerPoint processing", document_path)
    finally:
        # Clean up temporary files
        try:
            for file in os.listdir(temp_dir):
                os.remove(os.path.join(temp_dir, file))
            os.rmdir(temp_dir)
        except Exception as cleanup_error:
            logger.warning(f"Error cleaning up temporary files: {str(cleanup_error)}")


@mcp.tool(
    description="Read HTML file and extract text content, optionally extract links, images, and table information, and convert to Markdown format."
)
def mcpreadhtmltext(
    document_path: str = Field(description="Local HTML file path or Web URL."),
    extract_links: bool = Field(
        default=True, description="Whether to extract link information"
    ),
    extract_images: bool = Field(
        default=True, description="Whether to extract image information"
    ),
    extract_tables: bool = Field(
        default=True, description="Whether to extract table information"
    ),
    convert_to_markdown: bool = Field(
        default=True, description="Whether to convert HTML to Markdown format"
    ),
) -> str:
    """Read HTML file and extract text content, optionally extract links, images, and table information, and convert to Markdown format."""
    error = check_file_readable(document_path)
    if error:
        return DocumentError(error=error, file_path=document_path).model_dump_json()

    try:

        # Read HTML file
        with open(document_path, "r", encoding="utf-8") as f:
            html_content = f.read()

        # Parse HTML using BeautifulSoup
        soup = BeautifulSoup(html_content, "html.parser")

        # Extract text content (remove script and style content)
        for script in soup(["script", "style"]):
            script.extract()
        text_content = soup.get_text(separator="\n", strip=True)

        # Extract title
        title = soup.title.string if soup.title else None

        # Initialize result object
        result = HtmlDocument(
            content=text_content,
            html_content=html_content,
            file_path=document_path,
            file_name=os.path.basename(document_path),
            file_size=os.path.getsize(document_path),
            last_modified=datetime.fromtimestamp(
                os.path.getmtime(document_path)
            ).strftime("%Y-%m-%d %H:%M:%S"),
            title=title,
        )

        # Extract links
        if extract_links:
            links = []
            for link in soup.find_all("a"):
                href = link.get("href")
                text = link.get_text(strip=True)
                if href:
                    links.append({"url": href, "text": text})
            result.links = links

        # Extract images
        if extract_images:
            images = []
            for img in soup.find_all("img"):
                src = img.get("src")
                alt = img.get("alt", "")
                if src:
                    images.append({"src": src, "alt": alt})
            result.images = images

        # Extract tables
        if extract_tables:
            tables = []
            for table in soup.find_all("table"):
                tables.append(str(table))
            result.tables = tables

        # Convert to Markdown
        if convert_to_markdown:
            h = html2text.HTML2Text()
            h.ignore_links = False
            h.ignore_images = False
            h.ignore_tables = False
            markdown_content = h.handle(html_content)
            result.markdown = markdown_content

        return result.model_dump_json()

    except Exception as e:
        return handle_error(e, "HTML file reading", document_path)


def main():
    load_dotenv()

    print("Starting Document MCP Server...", file=sys.stderr)
    mcp.run(transport="stdio")


# Make the module callable
def __call__():
    """
    Make the module callable for uvx.
    This function is called when the module is executed directly.
    """
    main()


sys.modules[__name__].__call__ = __call__

# Run the server when the script is executed directly
if __name__ == "__main__":
    main()