feat: 增强编码能力 - 新增代码编辑器、代码搜索、Git集成

- 新增 plugins/code_editor.py: 精准代码编辑(搜索替换、批量编辑、diff对比)
- 新增 plugins/code_search.py: 代码搜索与理解(跨文件搜索、符号提取、依赖分析)
- 新增 plugins/git_tools.py: Git 版本控制(status/diff/log/commit/branch)
- 新增 core/ 模块: 配置管理、引擎管理、对话处理、显示输出、代码沙箱
- 修复 requirements.txt: 使用正确的 pip 包名(Pillow、opencv-python 等)
- 更新 unified_tool_manager.py: 兼容 Plugin 和 Liugin 两种类名
- 更新系统提示词: 增强编程工作流指导

本次更新由 mimo 自动化执行,与开发者无关。
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Xiaoli Dev committed 2026-04-25 10:55:27 +08:00
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"""
统一工具管理器 - 协调 Liugin、Skill 和 MCP 三套协议
自动判断并路由到正确的处理器
"""
import os
import json
import threading
from typing import Dict, List, Any, Optional, Union, Callable
from colorama import Fore, Style
class UnifiedToolManager:
"""
统一工具管理器
协调三种协议:
1. Liugin 协议 (plugins/) - 小狸自有协议
2. Skill 协议 (skills/) - Agent Skills 开源协议
3. MCP 协议 - Model Context Protocol (Anthropic/OpenAI 标准)
路由策略:
- 优先使用 Skill 协议(如果存在同名技能)
- 回退到 Liugin 协议
- 支持强制指定协议
- MCP 格式调用统一入口
"""
def __init__(self, cli_instance=None):
self.cli = cli_instance
self._lock = threading.RLock()
# 两个独立的加载器
self._liugin_manager = None
self._skill_loader = None
# 统一的工具注册表
self._tools: Dict[str, Dict[str, Any]] = {}
# 协议映射: tool_name -> protocol_type
self._protocol_map: Dict[str, str] = {}
# MCP 工具定义缓存
self._mcp_tools_cache: Optional[List[Dict]] = None
def initialize(self, plugins_dir: str = "plugins", skills_dir: str = "skills") -> None:
"""
初始化并加载所有工具
Args:
plugins_dir: Liugin 目录
skills_dir: Skill 目录
"""
# 加载 Liugins
self._load_plugins(plugins_dir)
# 加载 Skills
self._load_skills(skills_dir)
# 构建缓存
self._build_mcp_cache()
print(f"{Fore.GREEN}[统一工具管理器] 加载完成: {len(self._tools)} 个工具{Style.RESET_ALL}")
print(f" - Liugin 协议: {sum(1 for p in self._protocol_map.values() if p == 'liugin')}")
print(f" - Skill 协议: {sum(1 for p in self._protocol_map.values() if p == 'skill')}")
def _load_plugins(self, plugins_dir: str) -> None:
"""加载 Liugin 协议工具"""
import importlib.util
if not os.path.exists(plugins_dir):
return
# 获取启用的插件列表
enabled_plugins = self._get_enabled_plugins(plugins_dir)
for filename in os.listdir(plugins_dir):
if not filename.endswith('.py') or filename == '__init__.py':
continue
plugin_name = filename[:-3]
# 检查是否启用
if enabled_plugins is not None and plugin_name.lower() not in enabled_plugins:
continue
plugin_path = os.path.join(plugins_dir, filename)
try:
spec = importlib.util.spec_from_file_location(plugin_name, plugin_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# 兼容 Plugin 和 Liugin 两种类名
plugin_class = None
if hasattr(module, 'Liugin'):
plugin_class = module.Liugin
elif hasattr(module, 'Plugin'):
plugin_class = module.Plugin
if plugin_class:
plugin_instance = plugin_class()
# 设置 CLI 引用
if hasattr(plugin_instance, 'set_cli') and self.cli:
plugin_instance.set_cli(self.cli)
tool_info = plugin_instance.get_tool_info()
tool_name = tool_info.get('name', plugin_name)
# 获取 MCP 工具定义(如果插件提供)
mcp_definition = None
if hasattr(plugin_instance, 'get_mcp_definition'):
mcp_definition = plugin_instance.get_mcp_definition()
# 注册到统一工具表
self._tools[tool_name] = {
'name': tool_name,
'description': tool_info.get('description', ''),
'keywords': tool_info.get('keywords', []),
'usage': tool_info.get('usage', ''),
'handler': plugin_instance.handle,
'protocol': 'liugin',
'instance': plugin_instance,
'mcp_definition': mcp_definition
}
self._protocol_map[tool_name] = 'liugin'
except Exception as e:
print(f"{Fore.RED}加载 Liugin '{plugin_name}' 失败: {e}{Style.RESET_ALL}")
def _load_skills(self, skills_dir: str) -> None:
"""加载 Skill 协议工具 (支持 Markdown 和 Python 两种格式)"""
if not os.path.exists(skills_dir):
print(f"{Fore.YELLOW}Skills 目录不存在: {skills_dir}{Style.RESET_ALL}")
return
try:
from skills.base import SkillLoader, MarkdownSkill, Skill
self._skill_loader = SkillLoader(skills_dir)
# 先发现所有技能
discovered = self._skill_loader.discover_skills(skills_dir)
print(f"{Fore.CYAN}发现 {len(discovered)} 个技能: {[s['name'] for s in discovered]}{Style.RESET_ALL}")
# 加载所有技能
self._skill_loader.load_all_skills(skills_dir)
loaded_count = len(self._skill_loader.skills)
if loaded_count == 0:
print(f"{Fore.YELLOW}未加载任何 Skill 技能{Style.RESET_ALL}")
return
for skill_name, skill in self._skill_loader.skills.items():
# 设置 CLI 引用 (仅 Python 类技能)
if hasattr(skill, 'set_cli') and self.cli:
skill.set_cli(self.cli)
# 获取工具定义
tool_def = skill.get_tool_definition()
func_def = tool_def.get('function', {})
# 检查是否已有同名 Plugin,Skill 优先覆盖
if skill_name in self._tools:
old_protocol = self._protocol_map[skill_name]
print(f"{Fore.YELLOW}工具 '{skill_name}' 已存在({old_protocol}),使用 Skill 协议覆盖{Style.RESET_ALL}")
# 判断技能格式
is_markdown = isinstance(skill, MarkdownSkill)
skill_format = "markdown" if is_markdown else "python"
# 注册到统一工具表
self._tools[skill_name] = {
'name': skill_name,
'description': func_def.get('description', ''),
'keywords': getattr(skill, 'tags', []),
'usage': getattr(skill, 'instructions', '')[:500] if is_markdown else getattr(skill, 'usage', ''),
'handler': self._create_skill_handler(skill),
'protocol': 'skill',
'format': skill_format,
'instance': skill,
'tool_definition': tool_def,
'anthropic_definition': skill.get_anthropic_tool_definition(),
'mcp_definition': tool_def # Skill 的 OpenAI 格式就是 MCP 格式
}
self._protocol_map[skill_name] = 'skill'
print(f"{Fore.GREEN}已加载 Skill ({skill_format}): {skill_name}{Style.RESET_ALL}")
except ImportError as e:
print(f"{Fore.YELLOW}Skill 协议模块未找到: {e}{Style.RESET_ALL}")
except Exception as e:
print(f"{Fore.RED}加载 Skills 失败: {e}{Style.RESET_ALL}")
def _create_skill_handler(self, skill) -> callable:
"""
为 Skill 创建兼容 Plugin 的 handler
支持 Markdown 和 Python 两种技能格式
"""
from skills.base import MarkdownSkill, Skill
def handler(args: str) -> str:
import json
kwargs = {}
# 解析参数
if args.strip().startswith('{'):
try:
kwargs = json.loads(args)
except json.JSONDecodeError:
kwargs = {'operation': args}
else:
kwargs = {'operation': args}
# Markdown 技能: 返回指令供 AI 执行
if isinstance(skill, MarkdownSkill):
return f"[Skill: {skill.name}]\n{skill.instructions}"
# Python 技能: 直接执行
if hasattr(skill, 'run'):
result = skill.run(**kwargs)
if result.success:
return result.message or str(result.data)
else:
return f"错误: {result.error}"
return f"未知的技能类型: {type(skill)}"
return handler
def _get_enabled_plugins(self, plugins_dir: str) -> Optional[List[str]]:
"""获取启用的插件列表"""
# 检查环境变量
enabled_env = os.environ.get("XIAOLI_ENABLED_PLUGINS")
if enabled_env is not None:
if enabled_env == "":
return []
return [p.lower() for p in enabled_env.split(",")]
# 检查配置文件
config_file = os.path.join(os.path.dirname(plugins_dir), "plugins_config.py")
if os.path.exists(config_file):
try:
with open(config_file, 'r', encoding='utf-8') as f:
content = f.read()
if "# 不启用任何插件" in content:
return []
enabled = []
for line in content.split('\n'):
if line.startswith('ENABLE_') and '= True' in line:
name = line.split('=')[0].replace('ENABLE_', '').strip().lower()
enabled.append(name)
return enabled
except Exception:
pass
return None # 加载全部
def _build_mcp_cache(self) -> None:
"""构建 MCP 工具定义缓存(线程安全)"""
with self._lock:
self._mcp_tools_cache = []
for tool in self._tools.values():
mcp_def = tool.get('mcp_definition')
if mcp_def:
# 确保符合 MCP 标准
normalized = self._normalize_mcp_definition(mcp_def)
self._mcp_tools_cache.append(normalized)
else:
# 为没有 MCP 定义的 Plugin 生成默认定义
self._mcp_tools_cache.append(self._generate_mcp_definition(tool))
def _normalize_mcp_definition(self, mcp_def: Dict[str, Any]) -> Dict[str, Any]:
"""
标准化 MCP 工具定义
支持多种输入格式并转换为标准 MCP 格式
"""
# 如果已经是 MCP 格式(有 name 或 inputSchema)
if 'name' in mcp_def and ('inputSchema' in mcp_def or 'description' in mcp_def):
return mcp_def
# 如果是 OpenAI Function Calling 格式,转换
if mcp_def.get('type') == 'function' and 'function' in mcp_def:
func = mcp_def['function']
return {
"name": func.get('name', ''),
"description": func.get('description', ''),
"inputSchema": func.get('parameters', {
"type": "object",
"properties": {}
})
}
# 其他情况,直接返回
return mcp_def
def _generate_mcp_definition(self, tool: Dict[str, Any]) -> Dict[str, Any]:
"""
为 Plugin 生成符合 MCP 标准的工具定义
MCP 官方格式 (2025-03-26):
{
"name": "tool_name",
"title": "Human Readable Name", // optional
"description": "Human-readable description",
"inputSchema": {
"type": "object",
"properties": {...},
"required": [...]
},
"outputSchema": {...}, // optional
"annotations": {...} // optional
}
"""
return {
"name": tool['name'],
"description": tool.get('description', ''),
"inputSchema": {
"type": "object",
"properties": {
"args": {
"type": "string",
"description": f"命令参数。用法:\\n{tool.get('usage', '')[:1000]}"
}
},
"required": ["args"]
}
}
# ==================== 公共 API ====================
@property
def tools(self) -> List[Dict[str, Any]]:
"""获取所有工具列表(兼容 LiuginManager 接口)"""
return list(self._tools.values())
def get_tool(self, name: str) -> Optional[Dict[str, Any]]:
"""获取指定工具"""
return self._tools.get(name)
def get_protocol(self, name: str) -> Optional[str]:
"""获取工具使用的协议"""
return self._protocol_map.get(name)
def execute(self, tool_name: str, args: str = "", **kwargs) -> Dict[str, Any]:
"""
执行工具调用
Args:
tool_name: 工具名称
args: 参数字符串(Plugin 格式)
**kwargs: 关键字参数(Skill 格式或 MCP arguments)
Returns:
{"result": "..."} 格式的结果
"""
tool = self._tools.get(tool_name)
if not tool:
return {"result": f"未找到工具: {tool_name}"}
try:
handler = tool['handler']
protocol = tool['protocol']
instance = tool.get('instance')
if protocol == 'skill' and kwargs:
# Skill 协议,使用 kwargs
skill = tool['instance']
result = skill.run(**kwargs)
return {"result": result.message or str(result.data) if result.success else f"错误: {result.error}"}
elif protocol == 'liugin' and kwargs and not args:
# Liugin 协议但有 MCP 结构化参数,需要转换
# 检查插件是否有 convert_mcp_args 方法
if instance and hasattr(instance, 'convert_mcp_args'):
args = instance.convert_mcp_args(kwargs)
else:
args = self._convert_mcp_args_to_plugin_args(tool_name, kwargs, instance)
# 执行 handler
result = handler(args)
return {"result": result if result is not None else "无结果"}
except Exception as e:
return {"result": f"工具执行错误: {e}"}
# ==================== MCP 协议支持 ====================
def get_mcp_tools(self) -> List[Dict[str, Any]]:
"""
获取 MCP 格式的工具定义列表
MCP (Model Context Protocol) 使用 OpenAI Function Calling 格式
作为标准工具定义格式
Returns:
List of MCP tool definitions
"""
with self._lock:
if self._mcp_tools_cache is None:
self._build_mcp_cache()
return self._mcp_tools_cache.copy() # 返回副本,避免外部修改
def mcp_call(self, tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
"""
MCP 格式的工具调用入口
这是 MCP 协议的主要调用接口,接收结构化参数并返回结果
Args:
tool_name: 工具名称
arguments: 结构化参数 (JSON对象)
Returns:
MCP 标准响应格式:
{
"content": [
{
"type": "text",
"text": "结果内容"
}
],
"isError": false
}
"""
tool = self._tools.get(tool_name)
if not tool:
return self._mcp_error(f"未找到工具: {tool_name}")
try:
protocol = tool['protocol']
instance = tool.get('instance')
if protocol == 'liugin':
# Liugin 协议: 将 arguments 转换为 args 字符串
args = self._convert_mcp_args_to_plugin_args(tool_name, arguments, instance)
result = tool['handler'](args)
return self._mcp_success(result if result else "执行完成")
elif protocol == 'skill':
# Skill 协议: 直接传递 arguments 作为 kwargs
from skills.base import MarkdownSkill
if isinstance(instance, MarkdownSkill):
return self._mcp_success(f"[Skill: {instance.name}]\n{instance.instructions}")
if hasattr(instance, 'run'):
result = instance.run(**arguments)
if result.success:
return self._mcp_success(result.message or str(result.data))
else:
return self._mcp_error(f"执行失败: {result.error}")
return self._mcp_error(f"未知协议类型: {protocol}")
except Exception as e:
return self._mcp_error(f"工具执行错误: {str(e)}")
def _convert_mcp_args_to_plugin_args(self, tool_name: str, arguments: Dict[str, Any],
instance: Any) -> str:
"""
将 MCP 结构化参数转换为 Plugin 的 args 字符串
Args:
tool_name: 工具名称
arguments: MCP 参数对象
instance: 插件实例(可能有自定义转换方法)
Returns:
args 字符串
"""
# 检查插件是否有自定义的 MCP 参数转换方法
if instance and hasattr(instance, 'convert_mcp_args'):
return instance.convert_mcp_args(arguments)
# 默认转换策略
if 'args' in arguments:
# 如果有 args 字段,直接使用
return arguments['args']
# 将 arguments 转换为命令行格式
parts = []
# 第一个参数通常是操作/命令
if 'operation' in arguments:
parts.append(arguments['operation'])
elif 'action' in arguments:
parts.append(arguments['action'])
# 添加其他参数
for key, value in arguments.items():
if key in ('operation', 'action'):
continue
if isinstance(value, bool):
if value:
parts.append(f"-{key}")
elif isinstance(value, (list, tuple)):
for v in value:
parts.append(f"--{key}")
parts.append(self._quote_if_needed(str(v)))
else:
parts.append(f"--{key}")
parts.append(self._quote_if_needed(str(value)))
return ' '.join(parts)
def _quote_if_needed(self, s: str) -> str:
"""如果字符串包含空格,添加引号"""
if ' ' in s or '"' in s:
return f'"{s}"'
return s
def _mcp_success(self, text: str) -> Dict[str, Any]:
"""生成 MCP 成功响应"""
return {
"content": [
{
"type": "text",
"text": text
}
],
"isError": False
}
def _mcp_error(self, error_msg: str) -> Dict[str, Any]:
"""生成 MCP 错误响应"""
return {
"content": [
{
"type": "text",
"text": error_msg
}
],
"isError": True
}
# ==================== 兼容性 API ====================
def get_openai_tools(self) -> List[Dict[str, Any]]:
"""
获取 OpenAI Function Calling 格式的工具定义
OpenAI 格式:
{
"type": "function",
"function": {
"name": "...",
"description": "...",
"parameters": {...}
}
}
"""
tools = []
for mcp_def in self.get_mcp_tools():
tools.append({
"type": "function",
"function": {
"name": mcp_def.get("name", ""),
"description": mcp_def.get("description", ""),
"parameters": mcp_def.get("inputSchema", {
"type": "object",
"properties": {}
})
}
})
return tools
def get_anthropic_tools(self) -> List[Dict[str, Any]]:
"""
获取 Anthropic Tool Use 格式的工具定义
Anthropic 格式:
{
"name": "tool_name",
"description": "...",
"input_schema": {...}
}
"""
tools = []
for mcp_def in self.get_mcp_tools():
tools.append({
"name": mcp_def.get("name", ""),
"description": mcp_def.get("description", ""),
"input_schema": mcp_def.get("inputSchema", {
"type": "object",
"properties": {}
})
})
return tools
def search_tools(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
"""
搜索工具
Args:
query: 搜索关键词
limit: 返回数量限制
Returns:
匹配的工具列表
"""
query_lower = query.lower()
# 标准化:移除所有分隔符(连字符、下划线、空格)
query_normalized = query_lower.replace('-', '').replace('_', '').replace(' ', '')
matches = []
for tool in self._tools.values():
score = 0
name = tool['name'].lower()
name_normalized = name.replace('-', '').replace('_', '')
desc = tool['description'].lower()
keywords = [kw.lower() for kw in tool.get('keywords', [])]
# 名称完全匹配(忽略分隔符差异)
if query_lower == name or query_normalized == name_normalized:
score += 100
# 名称包含(标准化后匹配)
elif query_normalized in name_normalized or query_lower in name:
score += 50
# 描述包含
if query_lower in desc:
score += 30
# 关键词匹配
for kw in keywords:
kw_normalized = kw.replace('-', '').replace('_', '')
if query_lower in kw or query_normalized in kw_normalized:
score += 20
break
if score > 0:
matches.append({
'name': tool['name'],
'description': tool['description'],
'usage': tool.get('usage', ''),
'keywords': keywords,
'protocol': tool['protocol'],
'instance': tool.get('instance'),
'score': score
})
# 按分数排序
matches.sort(key=lambda x: x['score'], reverse=True)
return matches[:limit]
def register_tool(self, name: str, handler: callable, description: str = "",
protocol: str = "custom", mcp_definition: Dict = None, **kwargs) -> None:
"""
动态注册工具
Args:
name: 工具名称
handler: 处理函数
description: 描述
protocol: 协议类型
mcp_definition: MCP 工具定义(可选)
"""
with self._lock:
self._tools[name] = {
'name': name,
'description': description,
'handler': handler,
'protocol': protocol,
'mcp_definition': mcp_definition,
**kwargs
}
self._protocol_map[name] = protocol
# 更新缓存
if self._mcp_tools_cache is not None:
if mcp_definition:
self._mcp_tools_cache.append(mcp_definition)
else:
self._mcp_tools_cache.append(self._generate_mcp_definition(self._tools[name]))
def unregister_tool(self, name: str) -> bool:
"""注销工具"""
with self._lock:
if name in self._tools:
del self._tools[name]
del self._protocol_map[name]
# 重建缓存
self._build_mcp_cache()
return True
return False
def get_skills_system_prompt(self) -> str:
"""
获取所有技能的系统提示词
用于注入到 AI 的 system prompt 中,
让 AI 了解如何使用这些技能
"""
skill_prompts = []
for name, tool in self._tools.items():
if tool['protocol'] == 'skill':
instance = tool.get('instance')
if instance:
# 获取技能指令
instructions = getattr(instance, 'instructions', '')
scripts_dir = getattr(instance, 'scripts_dir', '')
prompt_parts = [f"## 技能: {name}"]
prompt_parts.append(f"描述: {tool['description']}")
if instructions:
# 截取关键部分,避免过长
prompt_parts.append(f"\n{instructions}")
if scripts_dir:
prompt_parts.append(f"\n脚本目录: {scripts_dir}")
skill_prompts.append('\n'.join(prompt_parts))
if skill_prompts:
header = "# 已加载的 Agent Skills\n以下技能可用于执行特定任务,调用方式与工具相同:\n"
return header + '\n\n---\n\n'.join(skill_prompts)
return ""
def get_tools_prompt(self) -> str:
"""
获取工具使用提示词(兼容旧接口)
包含 Plugin 和 Skill 两种协议的工具说明
"""
prompts = []
# Liugin 协议工具
plugin_tools = [t for t in self._tools.values() if t['protocol'] == 'liugin']
if plugin_tools:
prompts.append("## Liugin 工具")
for tool in plugin_tools:
usage = tool.get('usage', '')
if usage:
prompts.append(f"### {tool['name']}\n{usage}\n")
# Skill 协议工具(使用系统提示词)
skills_prompt = self.get_skills_system_prompt()
if skills_prompt:
prompts.append(skills_prompt)
return '\n\n'.join(prompts)
# ==================== MCP Server 模式支持 ====================
def get_mcp_server_info(self) -> Dict[str, Any]:
"""
获取 MCP Server 信息
用于 MCP 客户端连接时的初始化响应
"""
return {
"name": "xiaoli-tool-server",
"version": "1.0.0",
"protocolVersion": "2024-11-05",
"capabilities": {
"tools": {
"listChanged": True
}
}
}
def handle_mcp_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
"""
处理 MCP 协议请求
统一处理 MCP 客户端的各种请求
Args:
request: MCP 请求对象
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list" | "tools/call" | ...,
"params": {...}
}
Returns:
MCP 响应对象
{
"jsonrpc": "2.0",
"id": 1,
"result": {...}
}
"""
method = request.get('method', '')
params = request.get('params', {})
req_id = request.get('id')
result = None
error = None
try:
if method == 'tools/list':
result = {"tools": self.get_mcp_tools()}
elif method == 'tools/call':
tool_name = params.get('name')
arguments = params.get('arguments', {})
if not tool_name:
error = {"code": -32602, "message": "Missing tool name"}
else:
result = self.mcp_call(tool_name, arguments)
elif method == 'initialize':
result = self.get_mcp_server_info()
elif method == 'ping':
result = {}
else:
error = {"code": -32601, "message": f"Method not found: {method}"}
except Exception as e:
error = {"code": -32603, "message": str(e)}
response = {"jsonrpc": "2.0", "id": req_id}
if error:
response["error"] = error
else:
response["result"] = result
return response