982 lines
33 KiB
Python
982 lines
33 KiB
Python
#!/usr/bin/env python3
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"""
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多关键词搜索MCP服务器
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支持关键词数组匹配，按匹配数量排序输出
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参考json_reader_server.py的实现方式
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"""
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import json
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import os
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import sys
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import asyncio
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import re
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from typing import Any, Dict, List, Optional, Union
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from mcp_common import (
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get_allowed_directory,
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load_tools_from_json,
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resolve_file_path,
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find_file_in_project,
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is_regex_pattern,
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compile_pattern,
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create_error_response,
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create_success_response,
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create_initialize_response,
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create_ping_response,
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create_tools_list_response,
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handle_mcp_streaming
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)
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def parse_patterns_with_weights(patterns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""解析搜索模式列表，仅支持权重格式：
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[{"pattern": "keyword1", "weight": 2.0}, {"pattern": "/regex/", "weight": 0.5}]
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"""
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parsed_patterns = []
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for item in patterns:
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if not isinstance(item, dict):
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raise ValueError(f"Error: Search pattern must be in dictionary format with 'pattern' and 'weight' fields. Invalid item: {item}")
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pattern = item.get('pattern')
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weight = item.get('weight')
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if pattern is None:
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raise ValueError(f"Error: Missing 'pattern' field. Invalid item: {item}")
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if weight is None:
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raise ValueError(f"Error: Missing 'weight' field. Invalid item: {item}")
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# 确保权重是数字类型
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try:
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weight = float(weight)
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if weight <= 0:
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raise ValueError(f"Error: Weight must be a positive number. Invalid weight: {weight}")
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except (ValueError, TypeError):
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raise ValueError(f"Error: Weight must be a valid number. Invalid weight: {weight}")
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parsed_patterns.append({
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'pattern': pattern,
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'weight': weight
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})
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return parsed_patterns
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def search_count(patterns: List[Dict[str, Any]], file_paths: List[str],
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case_sensitive: bool = False) -> Dict[str, Any]:
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"""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"""
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if not patterns:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: Search pattern list cannot be empty"
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}
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]
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}
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# 解析搜索模式和权重
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try:
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parsed_patterns = parse_patterns_with_weights(patterns)
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except ValueError as e:
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return {
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"content": [
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{
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"type": "text",
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"text": str(e)
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}
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]
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}
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if not parsed_patterns:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: No valid search patterns"
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}
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]
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}
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if not file_paths:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: File path list cannot be empty"
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}
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]
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}
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# 预处理和验证搜索模式中的正则表达式
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valid_patterns = []
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regex_errors = []
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for pattern_info in parsed_patterns:
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pattern = pattern_info['pattern']
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compiled = compile_pattern(pattern)
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if compiled is None:
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regex_errors.append(pattern)
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else:
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valid_patterns.append({
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'pattern': pattern,
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'weight': pattern_info['weight'],
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'compiled_pattern': compiled
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})
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if regex_errors:
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error_msg = f"Warning: The following regular expressions failed to compile and will be ignored: {', '.join(regex_errors)}"
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print(error_msg)
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# 验证文件路径
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valid_paths = []
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for file_path in file_paths:
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try:
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# 解析文件路径，支持 folder/document.txt 和 document.txt 格式
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resolved_path = resolve_file_path(file_path)
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valid_paths.append(resolved_path)
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except Exception as e:
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continue
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if not valid_paths:
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return {
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"content": [
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{
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"type": "text",
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"text": f"Error: Specified files not found in project directory {project_data_dir}"
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}
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]
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}
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# 统计所有匹配结果
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all_results = []
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for file_path in valid_paths:
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try:
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results = search_patterns_in_file(file_path, valid_patterns, case_sensitive)
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all_results.extend(results)
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except Exception as e:
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continue
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# 计算统计信息
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total_lines_searched = 0
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total_weight_score = 0.0
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pattern_match_stats = {}
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file_match_stats = {}
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# 初始化模式统计
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for pattern_info in valid_patterns:
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pattern_key = pattern_info['pattern']
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pattern_match_stats[pattern_key] = {
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'match_count': 0,
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'weight_score': 0.0,
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'lines_matched': set()
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}
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# 统计所有文件行数
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for file_path in valid_paths:
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try:
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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lines = f.readlines()
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total_lines_searched += len(lines)
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except Exception:
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continue
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# 处理匹配结果
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for result in all_results:
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total_weight_score += result.get('weight_score', 0)
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# 文件级别统计
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file_path = result['file_path']
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if file_path not in file_match_stats:
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file_match_stats[file_path] = {
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'match_count': 0,
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'weight_score': 0.0,
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'lines_matched': set()
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}
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file_match_stats[file_path]['match_count'] += 1
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file_match_stats[file_path]['weight_score'] += result.get('weight_score', 0)
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file_match_stats[file_path]['lines_matched'].add(result['line_number'])
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# 模式级别统计
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for pattern in result['matched_patterns']:
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original_pattern = pattern['original']
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if original_pattern in pattern_match_stats:
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pattern_match_stats[original_pattern]['match_count'] += pattern['match_count']
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pattern_match_stats[original_pattern]['weight_score'] += pattern['weight_score']
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pattern_match_stats[original_pattern]['lines_matched'].add(result['line_number'])
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# 格式化统计输出
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formatted_lines = []
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formatted_lines.append("=== Matching Statistics Evaluation ===")
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formatted_lines.append(f"Files searched: {len(valid_paths)}")
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formatted_lines.append(f"Total lines searched: {total_lines_searched}")
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formatted_lines.append(f"Total matched lines: {len(all_results)}")
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formatted_lines.append(f"Total weight score: {total_weight_score:.2f}")
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formatted_lines.append(f"Match rate: {(len(all_results)/total_lines_searched*100):.2f}%" if total_lines_searched > 0 else "Match rate: 0.00%")
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formatted_lines.append("")
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# 按文件统计
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formatted_lines.append("=== Statistics by File ===")
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for file_path, stats in sorted(file_match_stats.items(), key=lambda x: x[1]['weight_score'], reverse=True):
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file_name = os.path.basename(file_path)
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formatted_lines.append(f"File: {file_name}")
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formatted_lines.append(f" Matched lines: {len(stats['lines_matched'])}")
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formatted_lines.append(f" Weight score: {stats['weight_score']:.2f}")
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formatted_lines.append("")
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# 按模式统计
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formatted_lines.append("=== Statistics by Pattern ===")
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for pattern, stats in sorted(pattern_match_stats.items(), key=lambda x: x[1]['weight_score'], reverse=True):
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formatted_lines.append(f"Pattern: {pattern}")
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formatted_lines.append(f" Match count: {stats['match_count']}")
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formatted_lines.append(f" Matched lines: {len(stats['lines_matched'])}")
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formatted_lines.append(f" Weight score: {stats['weight_score']:.2f}")
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formatted_lines.append("")
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formatted_output = "\n".join(formatted_lines)
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return {
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"content": [
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{
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"type": "text",
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"text": formatted_output
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}
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]
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}
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def search(patterns: List[Dict[str, Any]], file_paths: List[str],
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limit: int = 10, case_sensitive: bool = False) -> Dict[str, Any]:
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"""执行多模式搜索（关键词和正则表达式），必须包含权重"""
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if not patterns:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: Search pattern list cannot be empty"
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}
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]
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}
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# 解析搜索模式和权重
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try:
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parsed_patterns = parse_patterns_with_weights(patterns)
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except ValueError as e:
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return {
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"content": [
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{
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"type": "text",
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"text": str(e)
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}
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]
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}
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if not parsed_patterns:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: No valid search patterns"
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}
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]
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}
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if not file_paths:
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return {
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"content": [
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{
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"type": "text",
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"text": "Error: File path list cannot be empty"
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}
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]
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}
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# 预处理和验证搜索模式中的正则表达式
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valid_patterns = []
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regex_errors = []
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for pattern_info in parsed_patterns:
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pattern = pattern_info['pattern']
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compiled = compile_pattern(pattern)
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if compiled is None:
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regex_errors.append(pattern)
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else:
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valid_patterns.append({
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'pattern': pattern,
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'weight': pattern_info['weight'],
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'compiled_pattern': compiled
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})
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if regex_errors:
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error_msg = f"Warning: The following regular expressions failed to compile and will be ignored: {', '.join(regex_errors)}"
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print(error_msg)
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# 验证文件路径
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valid_paths = []
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for file_path in file_paths:
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try:
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# 解析文件路径，支持 folder/document.txt 和 document.txt 格式
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resolved_path = resolve_file_path(file_path)
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valid_paths.append(resolved_path)
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except Exception as e:
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continue
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if not valid_paths:
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return {
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"content": [
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{
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"type": "text",
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"text": f"Error: Specified files not found in project directory {project_data_dir}"
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}
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]
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}
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# 收集所有匹配结果
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all_results = []
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for file_path in valid_paths:
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try:
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results = search_patterns_in_file(file_path, valid_patterns, case_sensitive)
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all_results.extend(results)
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except Exception as e:
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continue
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# 按权重得分排序（降序），权重得分相同时按匹配数量排序
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all_results.sort(key=lambda x: (x.get('weight_score', 0), x['match_count']), reverse=True)
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# 限制结果数量
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limited_results = all_results[:limit]
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# 格式化输出
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if not limited_results:
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return {
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"content": [
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{
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"type": "text",
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"text": "No matching results found"
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}
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]
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}
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# 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
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formatted_lines = []
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# 第一行显示总匹配行数和当前显示数量
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total_matches = len(all_results)
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showing_count = len(limited_results)
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summary_line = f"Found {total_matches} matches, showing top {showing_count} results:"
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formatted_lines.append(summary_line)
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# 添加格式化的搜索结果
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for result in limited_results:
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weight_score = result.get('weight_score', 0)
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line_prefix = f"{result['line_number']}:weight({weight_score:.2f}):"
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# 构建匹配详情
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match_details = []
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for pattern in result['matched_patterns']:
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if pattern['type'] == 'regex':
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match_details.append(f"[regex:{pattern['original']}={pattern['match']}]")
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else:
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match_details.append(f"[keyword:{pattern['match']}]")
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match_info = " ".join(match_details) if match_details else ""
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formatted_line = f"{line_prefix}{match_info}:{result['content']}" if match_info else f"{line_prefix}{result['content']}"
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formatted_lines.append(formatted_line)
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formatted_output = "\n".join(formatted_lines)
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return {
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"content": [
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{
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"type": "text",
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"text": formatted_output
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}
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]
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}
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def search_patterns_in_file(file_path: str, patterns: List[Dict[str, Any]],
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case_sensitive: bool) -> List[Dict[str, Any]]:
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"""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"""
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results = []
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try:
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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lines = f.readlines()
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except Exception as e:
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return results
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# 预处理所有模式，包含权重信息
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processed_patterns = []
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for pattern_info in patterns:
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compiled = pattern_info['compiled_pattern']
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if compiled is not None: # 跳过无效的正则表达式
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processed_patterns.append({
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'original': pattern_info['pattern'],
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'pattern': compiled,
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'is_regex': isinstance(compiled, re.Pattern),
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'weight': pattern_info['weight']
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})
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for line_number, line in enumerate(lines, 1):
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line_content = line.rstrip('\n\r')
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search_line = line_content if case_sensitive else line_content.lower()
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# 统计匹配的模式数量和计算权重得分
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matched_patterns = []
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weight_score = 0.0
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for pattern_info in processed_patterns:
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pattern = pattern_info['pattern']
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is_regex = pattern_info['is_regex']
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weight = pattern_info['weight']
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match_found = False
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match_details = None
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match_count_in_line = 0
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if is_regex:
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# 正则表达式匹配
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if case_sensitive:
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matches = list(pattern.finditer(line_content))
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else:
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# 对于不区分大小写的正则，需要重新编译
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if isinstance(pattern, re.Pattern):
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# 创建不区分大小写的版本
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flags = pattern.flags | re.IGNORECASE
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case_insensitive_pattern = re.compile(pattern.pattern, flags)
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matches = list(case_insensitive_pattern.finditer(line_content))
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else:
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# 对于字符串模式，转换为小写再匹配
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search_pattern = pattern.lower() if isinstance(pattern, str) else pattern
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matches = list(re.finditer(search_pattern, search_line))
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if matches:
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match_found = True
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match_details = matches[0].group(0)
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# 重复正则匹配也只计算一次权重
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match_count_in_line = 1
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else:
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# 普通字符串匹配
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search_keyword = pattern if case_sensitive else pattern.lower()
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if search_keyword in search_line:
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match_found = True
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match_details = pattern
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# 重复关键词只计算一次权重
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match_count_in_line = 1
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if match_found:
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# 计算该模式的权重贡献 (权重 * 匹配次数)
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pattern_weight_score = weight * match_count_in_line
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weight_score += pattern_weight_score
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matched_patterns.append({
|
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'original': pattern_info['original'],
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'type': 'regex' if is_regex else 'keyword',
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'match': match_details,
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'weight': weight,
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'match_count': match_count_in_line,
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'weight_score': pattern_weight_score
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})
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if weight_score > 0:
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results.append({
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'line_number': line_number,
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'content': line_content,
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'match_count': len(matched_patterns),
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'weight_score': weight_score,
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'matched_patterns': matched_patterns,
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'file_path': file_path
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})
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return results
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|
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def regex_grep(patterns: Union[str, List[str]], file_paths: List[str], context_lines: int = 0,
|
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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"""
|
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# 处理模式输入
|
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if isinstance(patterns, str):
|
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patterns = [patterns]
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|
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# 验证模式列表
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|
if not patterns or not any(p.strip() for p in patterns):
|
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return {
|
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"content": [
|
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{
|
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"type": "text",
|
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"text": "Error: Patterns cannot be empty"
|
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}
|
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]
|
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}
|
|
|
|
# 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 {project_data_dir}"
|
|
}
|
|
]
|
|
}
|
|
|
|
# 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 {project_data_dir}"
|
|
}
|
|
]
|
|
}
|
|
|
|
# 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())
|