#!/usr/bin/env python3
"""
多关键词搜索MCP服务器
支持关键词数组匹配，按匹配数量排序输出
参考json_reader_server.py的实现方式
"""

import json
import os
import sys
import asyncio
import re
from typing import Any, Dict, List, Optional, Union
from mcp_common import (
    get_allowed_directory, 
    load_tools_from_json, 
    resolve_file_path,
    find_file_in_project,
    is_regex_pattern,
    compile_pattern,
    create_error_response,
    create_success_response,
    create_initialize_response,
    create_ping_response,
    create_tools_list_response,
    handle_mcp_streaming
)


def parse_patterns_with_weights(patterns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """解析搜索模式列表，仅支持权重格式：
    [{"pattern": "keyword1", "weight": 2.0}, {"pattern": "/regex/", "weight": 0.5}]
    """
    parsed_patterns = []
    
    for item in patterns:
        if not isinstance(item, dict):
            raise ValueError(f"Error: Search pattern must be in dictionary format with 'pattern' and 'weight' fields. Invalid item: {item}")
        
        pattern = item.get('pattern')
        weight = item.get('weight')
        
        if pattern is None:
            raise ValueError(f"Error: Missing 'pattern' field. Invalid item: {item}")
        
        if weight is None:
            raise ValueError(f"Error: Missing 'weight' field. Invalid item: {item}")
            
        # 确保权重是数字类型
        try:
            weight = float(weight)
            if weight <= 0:
                raise ValueError(f"Error: Weight must be a positive number. Invalid weight: {weight}")
        except (ValueError, TypeError):
            raise ValueError(f"Error: Weight must be a valid number. Invalid weight: {weight}")
            
        parsed_patterns.append({
            'pattern': pattern,
            'weight': weight
        })
    
    return parsed_patterns


def search_count(patterns: List[Dict[str, Any]], file_paths: List[str], 
               case_sensitive: bool = False) -> Dict[str, Any]:
    """统计多模式匹配数量评估（关键词和正则表达式），必须包含权重"""
    if not patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: Search pattern list cannot be empty"
                }
            ]
        }
    
    # 解析搜索模式和权重
    try:
        parsed_patterns = parse_patterns_with_weights(patterns)
    except ValueError as e:
        return {
            "content": [
                {
                    "type": "text",
                    "text": str(e)
                }
            ]
        }
    
    if not parsed_patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: No valid search patterns"
                }
            ]
        }
    
    if not file_paths:
        return {
            "content": [
                {
                    "type": "text", 
                    "text": "Error: File path list cannot be empty"
                }
            ]
        }
    
    # 预处理和验证搜索模式中的正则表达式
    valid_patterns = []
    regex_errors = []
    
    for pattern_info in parsed_patterns:
        pattern = pattern_info['pattern']
        compiled = compile_pattern(pattern)
        if compiled is None:
            regex_errors.append(pattern)
        else:
            valid_patterns.append({
                'pattern': pattern,
                'weight': pattern_info['weight'],
                'compiled_pattern': compiled
            })
    
    if regex_errors:
        error_msg = f"Warning: The following regular expressions failed to compile and will be ignored: {', '.join(regex_errors)}"
        print(error_msg)
    
    # 验证文件路径
    valid_paths = []
    for file_path in file_paths:
        try:
            # 解析文件路径，支持 folder/document.txt 和 document.txt 格式
            resolved_path = resolve_file_path(file_path)
            valid_paths.append(resolved_path)
        except Exception as e:
            continue
    
    if not valid_paths:
        return {
            "content": [
                {
                    "type": "text",
                    "text": f"Error: Specified files not found in project directory {get_allowed_directory()}"
                }
            ]
        }
    
    # 统计所有匹配结果
    all_results = []
    
    for file_path in valid_paths:
        try:
            results = search_patterns_in_file(file_path, valid_patterns, case_sensitive)
            all_results.extend(results)
        except Exception as e:
            continue
    
    # 计算统计信息
    total_lines_searched = 0
    total_weight_score = 0.0
    pattern_match_stats = {}
    file_match_stats = {}
    
    # 初始化模式统计
    for pattern_info in valid_patterns:
        pattern_key = pattern_info['pattern']
        pattern_match_stats[pattern_key] = {
            'match_count': 0,
            'weight_score': 0.0,
            'lines_matched': set()
        }
    
    # 统计所有文件行数
    for file_path in valid_paths:
        try:
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                lines = f.readlines()
                total_lines_searched += len(lines)
        except Exception:
            continue
    
    # 处理匹配结果
    for result in all_results:
        total_weight_score += result.get('weight_score', 0)
        
        # 文件级别统计
        file_path = result['file_path']
        if file_path not in file_match_stats:
            file_match_stats[file_path] = {
                'match_count': 0,
                'weight_score': 0.0,
                'lines_matched': set()
            }
        
        file_match_stats[file_path]['match_count'] += 1
        file_match_stats[file_path]['weight_score'] += result.get('weight_score', 0)
        file_match_stats[file_path]['lines_matched'].add(result['line_number'])
        
        # 模式级别统计
        for pattern in result['matched_patterns']:
            original_pattern = pattern['original']
            if original_pattern in pattern_match_stats:
                pattern_match_stats[original_pattern]['match_count'] += pattern['match_count']
                pattern_match_stats[original_pattern]['weight_score'] += pattern['weight_score']
                pattern_match_stats[original_pattern]['lines_matched'].add(result['line_number'])
    
    # 格式化统计输出
    formatted_lines = []
    formatted_lines.append("=== Matching Statistics Evaluation ===")
    formatted_lines.append(f"Files searched: {len(valid_paths)}")
    formatted_lines.append(f"Total lines searched: {total_lines_searched}")
    formatted_lines.append(f"Total matched lines: {len(all_results)}")
    formatted_lines.append(f"Total weight score: {total_weight_score:.2f}")
    formatted_lines.append(f"Match rate: {(len(all_results)/total_lines_searched*100):.2f}%" if total_lines_searched > 0 else "Match rate: 0.00%")
    formatted_lines.append("")
    
    # 按文件统计
    formatted_lines.append("=== Statistics by File ===")
    for file_path, stats in sorted(file_match_stats.items(), key=lambda x: x[1]['weight_score'], reverse=True):
        file_name = os.path.basename(file_path)
        formatted_lines.append(f"File: {file_name}")
        formatted_lines.append(f"  Matched lines: {len(stats['lines_matched'])}")
        formatted_lines.append(f"  Weight score: {stats['weight_score']:.2f}")
        formatted_lines.append("")
    
    # 按模式统计
    formatted_lines.append("=== Statistics by Pattern ===")
    for pattern, stats in sorted(pattern_match_stats.items(), key=lambda x: x[1]['weight_score'], reverse=True):
        formatted_lines.append(f"Pattern: {pattern}")
        formatted_lines.append(f"  Match count: {stats['match_count']}")
        formatted_lines.append(f"  Matched lines: {len(stats['lines_matched'])}")
        formatted_lines.append(f"  Weight score: {stats['weight_score']:.2f}")
        formatted_lines.append("")
    
    formatted_output = "\n".join(formatted_lines)
    
    return {
        "content": [
            {
                "type": "text",
                "text": formatted_output
            }
        ]
    }


def search(patterns: List[Dict[str, Any]], file_paths: List[str], 
          limit: int = 10, case_sensitive: bool = False) -> Dict[str, Any]:
    """执行多模式搜索（关键词和正则表达式），必须包含权重"""
    if not patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: Search pattern list cannot be empty"
                }
            ]
        }
    
    # 解析搜索模式和权重
    try:
        parsed_patterns = parse_patterns_with_weights(patterns)
    except ValueError as e:
        return {
            "content": [
                {
                    "type": "text",
                    "text": str(e)
                }
            ]
        }
    
    if not parsed_patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: No valid search patterns"
                }
            ]
        }
    
    if not file_paths:
        return {
            "content": [
                {
                    "type": "text", 
                    "text": "Error: File path list cannot be empty"
                }
            ]
        }
    
    # 预处理和验证搜索模式中的正则表达式
    valid_patterns = []
    regex_errors = []
    
    for pattern_info in parsed_patterns:
        pattern = pattern_info['pattern']
        compiled = compile_pattern(pattern)
        if compiled is None:
            regex_errors.append(pattern)
        else:
            valid_patterns.append({
                'pattern': pattern,
                'weight': pattern_info['weight'],
                'compiled_pattern': compiled
            })
    
    if regex_errors:
        error_msg = f"Warning: The following regular expressions failed to compile and will be ignored: {', '.join(regex_errors)}"
        print(error_msg)
    
    # 验证文件路径
    valid_paths = []
    for file_path in file_paths:
        try:
            # 解析文件路径，支持 folder/document.txt 和 document.txt 格式
            resolved_path = resolve_file_path(file_path)
            valid_paths.append(resolved_path)
        except Exception as e:
            continue
    
    if not valid_paths:
        return {
            "content": [
                {
                    "type": "text",
                    "text": f"Error: Specified files not found in project directory {get_allowed_directory()}"
                }
            ]
        }
    
    # 收集所有匹配结果
    all_results = []
    
    for file_path in valid_paths:
        try:
            results = search_patterns_in_file(file_path, valid_patterns, case_sensitive)
            all_results.extend(results)
        except Exception as e:
            continue
    
    # 按权重得分排序（降序），权重得分相同时按匹配数量排序
    all_results.sort(key=lambda x: (x.get('weight_score', 0), x['match_count']), reverse=True)
    
    # 限制结果数量
    limited_results = all_results[:limit]
    
    # 格式化输出
    if not limited_results:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "No matching results found"
                }
            ]
        }
    
    # 增强格式化输出，在第一行显示总匹配行数，然后显示权重得分、匹配类型和详细信息
    formatted_lines = []
    
    # 第一行显示总匹配行数和当前显示数量
    total_matches = len(all_results)
    showing_count = len(limited_results)
    summary_line = f"Found {total_matches} matches, showing top {showing_count} results:"
    formatted_lines.append(summary_line)
    
    # 添加格式化的搜索结果
    for result in limited_results:
        weight_score = result.get('weight_score', 0)
        line_prefix = f"{result['line_number']}:weight({weight_score:.2f}):"
        
        # 构建匹配详情
        match_details = []
        for pattern in result['matched_patterns']:
            if pattern['type'] == 'regex':
                match_details.append(f"[regex:{pattern['original']}={pattern['match']}]")
            else:
                match_details.append(f"[keyword:{pattern['match']}]")
        
        match_info = " ".join(match_details) if match_details else ""
        formatted_line = f"{line_prefix}{match_info}:{result['content']}" if match_info else f"{line_prefix}{result['content']}"
        formatted_lines.append(formatted_line)
    
    formatted_output = "\n".join(formatted_lines)
    
    return {
        "content": [
            {
                "type": "text",
                "text": formatted_output
            }
        ]
    }


def search_patterns_in_file(file_path: str, patterns: List[Dict[str, Any]], 
                          case_sensitive: bool) -> List[Dict[str, Any]]:
    """搜索单个文件中的搜索模式（关键词和正则表达式），支持权重计算"""
    results = []
    
    try:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
            lines = f.readlines()
    except Exception as e:
        return results
    
    # 预处理所有模式，包含权重信息
    processed_patterns = []
    for pattern_info in patterns:
        compiled = pattern_info['compiled_pattern']
        if compiled is not None:  # 跳过无效的正则表达式
            processed_patterns.append({
                'original': pattern_info['pattern'],
                'pattern': compiled,
                'is_regex': isinstance(compiled, re.Pattern),
                'weight': pattern_info['weight']
            })
    
    for line_number, line in enumerate(lines, 1):
        line_content = line.rstrip('\n\r')
        search_line = line_content if case_sensitive else line_content.lower()
        
        # 统计匹配的模式数量和计算权重得分
        matched_patterns = []
        weight_score = 0.0
        
        for pattern_info in processed_patterns:
            pattern = pattern_info['pattern']
            is_regex = pattern_info['is_regex']
            weight = pattern_info['weight']
            
            match_found = False
            match_details = None
            match_count_in_line = 0
            
            if is_regex:
                # 正则表达式匹配
                if case_sensitive:
                    matches = list(pattern.finditer(line_content))
                else:
                    # 对于不区分大小写的正则，需要重新编译
                    if isinstance(pattern, re.Pattern):
                        # 创建不区分大小写的版本
                        flags = pattern.flags | re.IGNORECASE
                        case_insensitive_pattern = re.compile(pattern.pattern, flags)
                        matches = list(case_insensitive_pattern.finditer(line_content))
                    else:
                        # 对于字符串模式，转换为小写再匹配
                        search_pattern = pattern.lower() if isinstance(pattern, str) else pattern
                        matches = list(re.finditer(search_pattern, search_line))
                
                if matches:
                    match_found = True
                    match_details = matches[0].group(0)
                    # 重复正则匹配也只计算一次权重
                    match_count_in_line = 1
            else:
                # 普通字符串匹配
                search_keyword = pattern if case_sensitive else pattern.lower()
                if search_keyword in search_line:
                    match_found = True
                    match_details = pattern
                    # 重复关键词只计算一次权重
                    match_count_in_line = 1
            
            if match_found:
                # 计算该模式的权重贡献 (权重 * 匹配次数)
                pattern_weight_score = weight * match_count_in_line
                weight_score += pattern_weight_score
                
                matched_patterns.append({
                    'original': pattern_info['original'],
                    'type': 'regex' if is_regex else 'keyword',
                    'match': match_details,
                    'weight': weight,
                    'match_count': match_count_in_line,
                    'weight_score': pattern_weight_score
                })
        
        if weight_score > 0:
            results.append({
                'line_number': line_number,
                'content': line_content,
                'match_count': len(matched_patterns),
                'weight_score': weight_score,
                'matched_patterns': matched_patterns,
                'file_path': file_path
            })
    
    return results




def regex_grep(patterns: Union[str, List[str]], file_paths: List[str], context_lines: int = 0, 
              case_sensitive: bool = False, limit: int = 50) -> Dict[str, Any]:
    """使用正则表达式搜索文件内容，支持多个模式和上下文行"""
    # 处理模式输入
    if isinstance(patterns, str):
        patterns = [patterns]
    
    # 验证模式列表
    if not patterns or not any(p.strip() for p in patterns):
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: Patterns cannot be empty"
                }
            ]
        }
    
    # 过滤空模式
    patterns = [p.strip() for p in patterns if p.strip()]
    
    if not file_paths:
        return {
            "content": [
                {
                    "type": "text", 
                    "text": "Error: File path list cannot be empty"
                }
            ]
        }
    
    # 编译正则表达式
    compiled_patterns = []
    for pattern in patterns:
        try:
            flags = 0 if case_sensitive else re.IGNORECASE
            compiled_pattern = re.compile(pattern, flags)
            compiled_patterns.append((pattern, compiled_pattern))
        except re.error as e:
            # 对于无效的正则表达式，跳过它但记录警告
            print(f"Warning: Invalid regular expression '{pattern}': {str(e)}, skipping...")
            continue
    
    if not compiled_patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: No valid regular expressions found"
                }
            ]
        }
    
    # 验证文件路径
    valid_paths = []
    for file_path in file_paths:
        try:
            # 解析文件路径，支持 folder/document.txt 和 document.txt 格式
            resolved_path = resolve_file_path(file_path)
            valid_paths.append(resolved_path)
        except Exception as e:
            continue
    
    if not valid_paths:
        return {
            "content": [
                {
                    "type": "text",
                    "text": f"Error: Specified files not found in project directory {get_allowed_directory()}"
                }
            ]
        }
    
    # 收集所有匹配结果
    all_results = []
    
    for file_path in valid_paths:
        try:
            for pattern, compiled_pattern in compiled_patterns:
                results = regex_search_in_file(file_path, compiled_pattern, context_lines, case_sensitive, pattern)
                all_results.extend(results)
        except Exception as e:
            continue
    
    # 按文件路径和行号排序
    all_results.sort(key=lambda x: (x['file_path'], x['match_line_number']))
    
    # 限制结果数量
    limited_results = all_results[:limit]
    
    # 格式化输出
    if not limited_results:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "No matches found"
                }
            ]
        }
    
    # 格式化输出
    formatted_lines = []
    
    # 显示总匹配数量和模式数量
    total_matches = len(all_results)
    showing_count = len(limited_results)
    summary_line = f"Found {total_matches} matches for {len(compiled_patterns)} patterns, showing top {showing_count} results:"
    formatted_lines.append(summary_line)
    
    # 按文件分组显示结果
    current_file = None
    for result in limited_results:
        file_path = result['file_path']
        if file_path != current_file:
            current_file = file_path
            file_name = os.path.basename(file_path)
            formatted_lines.append(f"\n--- File: {file_name} ---")
        
        match_line = result['match_line_number']
        match_text = result['match_text']
        matched_content = result['matched_content']
        pattern = result.get('pattern', 'unknown')
        
        # 显示匹配行和模式
        formatted_lines.append(f"{match_line}[pattern: {pattern}]:{matched_content}")
        
        # 显示上下文行
        if 'context_before' in result:
            for context_line in result['context_before']:
                formatted_lines.append(f"{context_line['line_number']}:{context_line['content']}")
        
        if 'context_after' in result:
            for context_line in result['context_after']:
                formatted_lines.append(f"{context_line['line_number']}:{context_line['content']}")
    
    formatted_output = "\n".join(formatted_lines)
    
    return {
        "content": [
            {
                "type": "text",
                "text": formatted_output
            }
        ]
    }


def regex_grep_count(patterns: Union[str, List[str]], file_paths: List[str], 
                    case_sensitive: bool = False) -> Dict[str, Any]:
    """使用正则表达式统计匹配数量，支持多个模式"""
    # 处理模式输入
    if isinstance(patterns, str):
        patterns = [patterns]
    
    # 验证模式列表
    if not patterns or not any(p.strip() for p in patterns):
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: Patterns cannot be empty"
                }
            ]
        }
    
    # 过滤空模式
    patterns = [p.strip() for p in patterns if p.strip()]
    
    if not file_paths:
        return {
            "content": [
                {
                    "type": "text", 
                    "text": "Error: File path list cannot be empty"
                }
            ]
        }
    
    # 编译正则表达式
    compiled_patterns = []
    for pattern in patterns:
        try:
            flags = 0 if case_sensitive else re.IGNORECASE
            compiled_pattern = re.compile(pattern, flags)
            compiled_patterns.append((pattern, compiled_pattern))
        except re.error as e:
            # 对于无效的正则表达式，跳过它但记录警告
            print(f"Warning: Invalid regular expression '{pattern}': {str(e)}, skipping...")
            continue
    
    if not compiled_patterns:
        return {
            "content": [
                {
                    "type": "text",
                    "text": "Error: No valid regular expressions found"
                }
            ]
        }
    
    # 验证文件路径
    valid_paths = []
    for file_path in file_paths:
        try:
            # 解析文件路径，支持 folder/document.txt 和 document.txt 格式
            resolved_path = resolve_file_path(file_path)
            valid_paths.append(resolved_path)
        except Exception as e:
            continue
    
    if not valid_paths:
        return {
            "content": [
                {
                    "type": "text",
                    "text": f"Error: Specified files not found in project directory {get_allowed_directory()}"
                }
            ]
        }
    
    # 统计匹配结果
    total_matches = 0
    total_lines_with_matches = 0
    file_stats = {}
    pattern_stats = {}
    
    # 初始化模式统计
    for pattern, _ in compiled_patterns:
        pattern_stats[pattern] = {
            'matches': 0,
            'lines_with_matches': 0
        }
    
    for file_path in valid_paths:
        file_name = os.path.basename(file_path)
        file_matches = 0
        file_lines_with_matches = 0
        
        try:
            for pattern, compiled_pattern in compiled_patterns:
                matches, lines_with_matches = regex_count_in_file(file_path, compiled_pattern, case_sensitive)
                total_matches += matches
                total_lines_with_matches += lines_with_matches
                file_matches += matches
                file_lines_with_matches = max(file_lines_with_matches, lines_with_matches)  # 避免重复计算行数
                
                # 更新模式统计
                pattern_stats[pattern]['matches'] += matches
                pattern_stats[pattern]['lines_with_matches'] += lines_with_matches
            
            file_stats[file_name] = {
                'matches': file_matches,
                'lines_with_matches': file_lines_with_matches
            }
        except Exception as e:
            continue
    
    # 格式化输出
    formatted_lines = []
    formatted_lines.append("=== Regex Match Statistics ===")
    formatted_lines.append(f"Patterns: {', '.join([p for p, _ in compiled_patterns])}")
    formatted_lines.append(f"Files searched: {len(valid_paths)}")
    formatted_lines.append(f"Total matches: {total_matches}")
    formatted_lines.append(f"Total lines with matches: {total_lines_with_matches}")
    formatted_lines.append("")
    
    # 按模式统计
    formatted_lines.append("=== Statistics by Pattern ===")
    for pattern, stats in sorted(pattern_stats.items()):
        formatted_lines.append(f"Pattern: {pattern}")
        formatted_lines.append(f"  Matches: {stats['matches']}")
        formatted_lines.append(f"  Lines with matches: {stats['lines_with_matches']}")
        formatted_lines.append("")
    
    # 按文件统计
    formatted_lines.append("=== Statistics by File ===")
    for file_name, stats in sorted(file_stats.items()):
        formatted_lines.append(f"File: {file_name}")
        formatted_lines.append(f"  Matches: {stats['matches']}")
        formatted_lines.append(f"  Lines with matches: {stats['lines_with_matches']}")
        formatted_lines.append("")
    
    formatted_output = "\n".join(formatted_lines)
    
    return {
        "content": [
            {
                "type": "text",
                "text": formatted_output
            }
        ]
    }


def regex_search_in_file(file_path: str, pattern: re.Pattern, 
                        context_lines: int, case_sensitive: bool, pattern_str: str = None) -> List[Dict[str, Any]]:
    """在单个文件中搜索正则表达式，支持上下文"""
    results = []
    
    try:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
            lines = f.readlines()
    except Exception as e:
        return results
    
    for line_number, line in enumerate(lines, 1):
        line_content = line.rstrip('\n\r')
        
        # 搜索匹配
        matches = list(pattern.finditer(line_content))
        if matches:
            # 准备上下文
            context_before = []
            context_after = []
            
            if context_lines > 0:
                # 获取前面的上下文
                start_line = max(0, line_number - 1 - context_lines)
                for i in range(start_line, line_number - 1):
                    if i < len(lines):
                        context_before.append({
                            'line_number': i + 1,
                            'content': lines[i].rstrip('\n\r')
                        })
                
                # 获取后面的上下文
                end_line = min(len(lines), line_number + context_lines)
                for i in range(line_number, end_line):
                    if i < len(lines):
                        context_after.append({
                            'line_number': i + 1,
                            'content': lines[i].rstrip('\n\r')
                        })
            
            # 为每个匹配创建结果
            for match in matches:
                result = {
                    'file_path': file_path,
                    'match_line_number': line_number,
                    'match_text': line_content,
                    'matched_content': match.group(0),
                    'pattern': pattern_str or 'unknown',
                    'start_pos': match.start(),
                    'end_pos': match.end()
                }
                
                if context_before:
                    result['context_before'] = context_before
                
                if context_after:
                    result['context_after'] = context_after
                
                results.append(result)
    
    return results


def regex_count_in_file(file_path: str, pattern: re.Pattern, 
                       case_sensitive: bool) -> tuple[int, int]:
    """统计文件中的匹配数量"""
    total_matches = 0
    lines_with_matches = 0
    
    try:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
            lines = f.readlines()
    except Exception as e:
        return total_matches, lines_with_matches
    
    for line_number, line in enumerate(lines, 1):
        line_content = line.rstrip('\n\r')
        
        # 搜索匹配
        matches = list(pattern.finditer(line_content))
        if matches:
            total_matches += len(matches)
            lines_with_matches += 1
    
    return total_matches, lines_with_matches


async def handle_request(request: Dict[str, Any]) -> Dict[str, Any]:
    """Handle MCP request"""
    try:
        method = request.get("method")
        params = request.get("params", {})
        request_id = request.get("id")
        
        if method == "initialize":
            return create_initialize_response(request_id, "multi-keyword-search")
        
        elif method == "ping":
            return create_ping_response(request_id)
        
        elif method == "tools/list":
            # 从 JSON 文件加载工具定义
            tools = load_tools_from_json("multi_keyword_search_tools.json")
            return create_tools_list_response(request_id, tools)
        
        elif method == "tools/call":
            tool_name = params.get("name")
            arguments = params.get("arguments", {})
            
            if tool_name == "search":
                patterns = arguments.get("patterns", [])
                file_paths = arguments.get("file_paths", [])
                limit = arguments.get("limit", 10)
                case_sensitive = arguments.get("case_sensitive", False)
                
                result = search(patterns, file_paths, limit, case_sensitive)
                
                return {
                    "jsonrpc": "2.0",
                    "id": request_id,
                    "result": result
                }
            
            elif tool_name == "search_count":
                patterns = arguments.get("patterns", [])
                file_paths = arguments.get("file_paths", [])
                case_sensitive = arguments.get("case_sensitive", False)
                
                result = search_count(patterns, file_paths, case_sensitive)
                
                return {
                    "jsonrpc": "2.0",
                    "id": request_id,
                    "result": result
                }
            
            elif tool_name == "regex_grep":
                patterns = arguments.get("patterns", [])
                # 兼容旧的pattern参数
                if not patterns and "pattern" in arguments:
                    patterns = arguments.get("pattern", "")
                file_paths = arguments.get("file_paths", [])
                context_lines = arguments.get("context_lines", 0)
                case_sensitive = arguments.get("case_sensitive", False)
                limit = arguments.get("limit", 50)
                
                result = regex_grep(patterns, file_paths, context_lines, case_sensitive, limit)
                
                return {
                    "jsonrpc": "2.0",
                    "id": request_id,
                    "result": result
                }
            
            elif tool_name == "regex_grep_count":
                patterns = arguments.get("patterns", [])
                # 兼容旧的pattern参数
                if not patterns and "pattern" in arguments:
                    patterns = arguments.get("pattern", "")
                file_paths = arguments.get("file_paths", [])
                case_sensitive = arguments.get("case_sensitive", False)
                
                result = regex_grep_count(patterns, file_paths, case_sensitive)
                
                return {
                    "jsonrpc": "2.0",
                    "id": request_id,
                    "result": result
                }
            
            else:
                return create_error_response(request_id, -32601, f"Unknown tool: {tool_name}")
        
        else:
            return create_error_response(request_id, -32601, f"Unknown method: {method}")
    
    except Exception as e:
        return create_error_response(request.get("id"), -32603, f"Internal error: {str(e)}")


async def main():
    """Main entry point."""
    await handle_mcp_streaming(handle_request)


if __name__ == "__main__":
    asyncio.run(main())
