agent / backend /backend_app /helloAgents /tools /builtin /protocol_tools.py
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"""
协议工具集合
提供基于协议实现的工具接口:
- MCP Tool: 基于 fastmcp 库,用于连接和调用 MCP 服务器
- A2A Tool: 基于官方 a2a 库,用于 Agent 间通信(需要安装 a2a)
- ANP Tool: 基于概念实现,用于服务发现和网络管理
"""
from typing import Dict, Any, List, Optional
from ..base import Tool, ToolParameter
import os
# MCP服务器环境变量映射表
# 用于自动检测常见MCP服务器需要的环境变量
MCP_SERVER_ENV_MAP = {
"server-github": ["GITHUB_PERSONAL_ACCESS_TOKEN"],
"server-slack": ["SLACK_BOT_TOKEN", "SLACK_TEAM_ID"],
"server-google-drive": ["GOOGLE_CLIENT_ID", "GOOGLE_CLIENT_SECRET", "GOOGLE_REFRESH_TOKEN"],
"server-postgres": ["POSTGRES_CONNECTION_STRING"],
"server-sqlite": [], # 不需要环境变量
"server-filesystem": [], # 不需要环境变量
}
class MCPTool(Tool):
"""MCP (Model Context Protocol) 工具
连接到 MCP 服务器并调用其提供的工具、资源和提示词。
功能:
- 列出服务器提供的工具
- 调用服务器工具
- 读取服务器资源
- 获取提示词模板
使用示例:
>>> from hello_agents.tools.builtin import MCPTool
>>>
>>> # 方式1: 使用内置演示服务器
>>> tool = MCPTool() # 自动创建内置服务器
>>> result = tool.run({"action": "list_tools"})
>>>
>>> # 方式2: 连接到外部 MCP 服务器
>>> tool = MCPTool(server_command=["python", "examples/mcp_example.py"])
>>> result = tool.run({"action": "list_tools"})
>>>
>>> # 方式3: 使用自定义 FastMCP 服务器
>>> from fastmcp import FastMCP
>>> server = FastMCP("MyServer")
>>> tool = MCPTool(server=server)
注意:使用 fastmcp 库,已包含在依赖中
"""
def __init__(self,
name: str = "mcp",
description: Optional[str] = None,
server_command: Optional[List[str]] = None,
server_args: Optional[List[str]] = None,
server: Optional[Any] = None,
auto_expand: bool = True,
env: Optional[Dict[str, str]] = None,
env_keys: Optional[List[str]] = None):
"""
初始化 MCP 工具
Args:
name: 工具名称(默认为"mcp",建议为不同服务器指定不同名称)
description: 工具描述(可选,默认为通用描述)
server_command: 服务器启动命令(如 ["python", "server.py"])
server_args: 服务器参数列表
server: FastMCP 服务器实例(可选,用于内存传输)
auto_expand: 是否自动展开为独立工具(默认True)
env: 环境变量字典(优先级最高,直接传递给MCP服务器)
env_keys: 要从系统环境变量加载的key列表(优先级中等)
环境变量优先级(从高到低):
1. 直接传递的env参数
2. env_keys指定的环境变量
3. 自动检测的环境变量(根据server_command)
注意:如果所有参数都为空,将创建内置演示服务器
示例:
>>> # 方式1:直接传递环境变量(优先级最高)
>>> github_tool = MCPTool(
... name="github",
... server_command=["npx", "-y", "@modelcontextprotocol/server-github"],
... env={"GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxx"}
... )
>>>
>>> # 方式2:从.env文件加载指定的环境变量
>>> github_tool = MCPTool(
... name="github",
... server_command=["npx", "-y", "@modelcontextprotocol/server-github"],
... env_keys=["GITHUB_PERSONAL_ACCESS_TOKEN"]
... )
>>>
>>> # 方式3:自动检测(最简单,推荐)
>>> github_tool = MCPTool(
... name="github",
... server_command=["npx", "-y", "@modelcontextprotocol/server-github"]
... # 自动从环境变量加载GITHUB_PERSONAL_ACCESS_TOKEN
... )
"""
self.server_command = server_command
self.server_args = server_args or []
self.server = server
self._client = None
self._available_tools = []
self.auto_expand = auto_expand
self.prefix = f"{name}_" if auto_expand else ""
# 环境变量处理(优先级:env > env_keys > 自动检测)
self.env = self._prepare_env(env, env_keys, server_command)
# 如果没有指定任何服务器,创建内置演示服务器
if not server_command and not server:
self.server = self._create_builtin_server()
# 自动发现工具
self._discover_tools()
# 设置默认描述或自动生成
if description is None:
description = self._generate_description()
super().__init__(
name=name,
description=description,
expandable=auto_expand
)
def _prepare_env(self,
env: Optional[Dict[str, str]],
env_keys: Optional[List[str]],
server_command: Optional[List[str]]) -> Dict[str, str]:
"""
准备环境变量
优先级:env > env_keys > 自动检测
Args:
env: 直接传递的环境变量字典
env_keys: 要从系统环境变量加载的key列表
server_command: 服务器命令(用于自动检测)
Returns:
合并后的环境变量字典
"""
result_env = {}
# 1. 自动检测(优先级最低)
if server_command:
# 从命令中提取服务器名称
server_name = None
for part in server_command:
if "server-" in part:
# 提取类似 "@modelcontextprotocol/server-github" 中的 "server-github"
server_name = part.split("/")[-1] if "/" in part else part
break
# 查找映射表
if server_name and server_name in MCP_SERVER_ENV_MAP:
auto_keys = MCP_SERVER_ENV_MAP[server_name]
for key in auto_keys:
value = os.getenv(key)
if value:
result_env[key] = value
print(f"🔑 自动加载环境变量: {key}")
# 2. env_keys指定的环境变量(优先级中等)
if env_keys:
for key in env_keys:
value = os.getenv(key)
if value:
result_env[key] = value
print(f"🔑 从env_keys加载环境变量: {key}")
else:
print(f"⚠️ 警告: 环境变量 {key} 未设置")
# 3. 直接传递的env(优先级最高)
if env:
result_env.update(env)
for key in env.keys():
print(f"🔑 使用直接传递的环境变量: {key}")
return result_env
def _create_builtin_server(self):
"""创建内置演示服务器"""
try:
from fastmcp import FastMCP
server = FastMCP("HelloAgents-BuiltinServer")
@server.tool()
def add(a: float, b: float) -> float:
"""加法计算器"""
return a + b
@server.tool()
def subtract(a: float, b: float) -> float:
"""减法计算器"""
return a - b
@server.tool()
def multiply(a: float, b: float) -> float:
"""乘法计算器"""
return a * b
@server.tool()
def divide(a: float, b: float) -> float:
"""除法计算器"""
if b == 0:
raise ValueError("除数不能为零")
return a / b
@server.tool()
def greet(name: str = "World") -> str:
"""友好问候"""
return f"Hello, {name}! 欢迎使用 HelloAgents MCP 工具!"
@server.tool()
def get_system_info() -> dict:
"""获取系统信息"""
import platform
import sys
return {
"platform": platform.system(),
"python_version": sys.version,
"server_name": "HelloAgents-BuiltinServer",
"tools_count": 6
}
return server
except ImportError:
raise ImportError(
"创建内置 MCP 服务器需要 fastmcp 库。请安装: pip install fastmcp"
)
def _discover_tools(self):
"""发现MCP服务器提供的所有工具"""
try:
from helloAgents.protocols.mcp.client import MCPClient
import asyncio
async def discover():
client_source = self.server if self.server else self.server_command
async with MCPClient(client_source, self.server_args, env=self.env) as client:
tools = await client.list_tools()
return tools
# 运行异步发现
try:
loop = asyncio.get_running_loop()
# 如果已有循环,在新线程中运行
import concurrent.futures
def run_in_thread():
new_loop = asyncio.new_event_loop()
asyncio.set_event_loop(new_loop)
try:
return new_loop.run_until_complete(discover())
finally:
new_loop.close()
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(run_in_thread)
self._available_tools = future.result()
except RuntimeError:
# 没有运行中的循环
self._available_tools = asyncio.run(discover())
except Exception as e:
# 工具发现失败不影响初始化
self._available_tools = []
def _generate_description(self) -> str:
"""生成增强的工具描述"""
if not self._available_tools:
return "连接到 MCP 服务器,调用工具、读取资源和获取提示词。支持内置服务器和外部服务器。"
if self.auto_expand:
# 展开模式:简单描述
return f"MCP工具服务器,包含{len(self._available_tools)}个工具。这些工具会自动展开为独立的工具供Agent使用。"
else:
# 非展开模式:详细描述
desc_parts = [
f"MCP工具服务器,提供{len(self._available_tools)}个工具:"
]
# 列出所有工具
for tool in self._available_tools:
tool_name = tool.get('name', 'unknown')
tool_desc = tool.get('description', '无描述')
# 简化描述,只取第一句
short_desc = tool_desc.split('.')[0] if tool_desc else '无描述'
desc_parts.append(f" • {tool_name}: {short_desc}")
# 添加调用格式说明
desc_parts.append("\n调用格式:返回JSON格式的参数")
desc_parts.append('{"action": "call_tool", "tool_name": "工具名", "arguments": {...}}')
# 添加示例
if self._available_tools:
first_tool = self._available_tools[0]
tool_name = first_tool.get('name', 'example')
desc_parts.append(f'\n示例:{{"action": "call_tool", "tool_name": "{tool_name}", "arguments": {{...}}}}')
return "\n".join(desc_parts)
def get_expanded_tools(self) -> List['Tool']: # type: ignore
"""
获取展开的工具列表
将MCP服务器的每个工具包装成独立的Tool对象
Returns:
Tool对象列表
"""
if not self.auto_expand:
return []
from .mcp_wrapper_tool import MCPWrappedTool
expanded_tools = []
for tool_info in self._available_tools:
wrapped_tool = MCPWrappedTool(
mcp_tool=self,
tool_info=tool_info,
prefix=self.prefix
)
expanded_tools.append(wrapped_tool)
return expanded_tools
def run(self, parameters: Dict[str, Any]) -> str:
"""
同步可调试执行 MCP 操作(无异步、不跳断点、正常执行)
"""
print("🔥 已进入 MCPTool.run 方法")
print("参数:", parameters)
from helloAgents.protocols.mcp.client import MCPClient
import asyncio
action = parameters.get("action", "").lower()
if not action and "tool_name" in parameters:
action = "call_tool"
parameters["action"] = action
if not action:
return "错误:必须指定 action"
try:
# 同步执行异步函数(不会跳断点、不会跑子线程)
async def task():
client_source = self.server or self.server_command
async with MCPClient(client_source, self.server_args, env=self.env) as client:
if action == "list_tools":
tools = await client.list_tools()
return "\n".join([f"- {t['name']}: {t['description']}" for t in tools])
elif action == "call_tool":
tool_name = parameters.get("tool_name")
args = parameters.get("arguments", {})
return await client.call_tool(tool_name, args)
return "不支持的操作"
# 最干净的同步运行
return asyncio.run(task())
except Exception as e:
return f"MCP 执行失败:{str(e)}"
# def run(self, parameters: Dict[str, Any]) -> str:
# """
# 执行 MCP 操作
# Args:
# parameters: 包含以下参数的字典
# - action: 操作类型 (list_tools, call_tool, list_resources, read_resource, list_prompts, get_prompt)
# 如果不指定action但指定了tool_name,会自动推断为call_tool
# - tool_name: 工具名称(call_tool 需要)
# - arguments: 工具参数(call_tool 需要)
# - uri: 资源 URI(read_resource 需要)
# - prompt_name: 提示词名称(get_prompt 需要)
# - prompt_arguments: 提示词参数(get_prompt 可选)
# Returns:
# 操作结果
# """
# from helloAgents.protocols.mcp.client import MCPClient
# # 智能推断action:如果没有action但有tool_name,自动设置为call_tool
# action = parameters.get("action", "").lower()
# if not action and "tool_name" in parameters:
# action = "call_tool"
# parameters["action"] = action
# if not action:
# return "错误:必须指定 action 参数或 tool_name 参数"
# try:
# # 使用增强的异步客户端
# import asyncio
# from helloAgents.protocols.mcp.client import MCPClient
# async def run_mcp_operation():
# # 根据配置选择客户端创建方式
# if self.server:
# # 使用内置服务器(内存传输)
# client_source = self.server
# else:
# # 使用外部服务器命令
# client_source = self.server_command
# async with MCPClient(client_source, self.server_args, env=self.env) as client:
# if action == "list_tools":
# tools = await client.list_tools()
# if not tools:
# return "没有找到可用的工具"
# result = f"找到 {len(tools)} 个工具:\n"
# for tool in tools:
# result += f"- {tool['name']}: {tool['description']}\n"
# return result
# elif action == "call_tool":
# tool_name = parameters.get("tool_name")
# arguments = parameters.get("arguments", {})
# if not tool_name:
# return "错误:必须指定 tool_name 参数"
# result = await client.call_tool(tool_name, arguments)
# return f"工具 '{tool_name}' 执行结果:\n{result}"
# elif action == "list_resources":
# resources = await client.list_resources()
# if not resources:
# return "没有找到可用的资源"
# result = f"找到 {len(resources)} 个资源:\n"
# for resource in resources:
# result += f"- {resource['uri']}: {resource['name']}\n"
# return result
# elif action == "read_resource":
# uri = parameters.get("uri")
# if not uri:
# return "错误:必须指定 uri 参数"
# content = await client.read_resource(uri)
# return f"资源 '{uri}' 内容:\n{content}"
# elif action == "list_prompts":
# prompts = await client.list_prompts()
# if not prompts:
# return "没有找到可用的提示词"
# result = f"找到 {len(prompts)} 个提示词:\n"
# for prompt in prompts:
# result += f"- {prompt['name']}: {prompt['description']}\n"
# return result
# elif action == "get_prompt":
# prompt_name = parameters.get("prompt_name")
# prompt_arguments = parameters.get("prompt_arguments", {})
# if not prompt_name:
# return "错误:必须指定 prompt_name 参数"
# messages = await client.get_prompt(prompt_name, prompt_arguments)
# result = f"提示词 '{prompt_name}':\n"
# for msg in messages:
# result += f"[{msg['role']}] {msg['content']}\n"
# return result
# else:
# return f"错误:不支持的操作 '{action}'"
# # 运行异步操作
# try:
# # 检查是否已有运行中的事件循环
# try:
# loop = asyncio.get_running_loop()
# # 如果有运行中的循环,在新线程中运行新的事件循环
# import concurrent.futures
# import threading
# def run_in_thread():
# # 在新线程中创建新的事件循环
# new_loop = asyncio.new_event_loop()
# asyncio.set_event_loop(new_loop)
# try:
# return new_loop.run_until_complete(run_mcp_operation())
# finally:
# new_loop.close()
# with concurrent.futures.ThreadPoolExecutor() as executor:
# future = executor.submit(run_in_thread)
# return future.result()
# except RuntimeError:
# # 没有运行中的循环,直接运行
# return asyncio.run(run_mcp_operation())
# except Exception as e:
# return f"异步操作失败: {str(e)}"
# except Exception as e:
# return f"MCP 操作失败: {str(e)}"
def get_parameters(self) -> List[ToolParameter]:
"""获取工具参数定义"""
return [
ToolParameter(
name="action",
type="string",
description="操作类型: list_tools, call_tool, list_resources, read_resource, list_prompts, get_prompt",
required=True
),
ToolParameter(
name="tool_name",
type="string",
description="工具名称(call_tool 操作需要)",
required=False
),
ToolParameter(
name="arguments",
type="object",
description="工具参数(call_tool 操作需要)",
required=False
),
ToolParameter(
name="uri",
type="string",
description="资源 URI(read_resource 操作需要)",
required=False
),
ToolParameter(
name="prompt_name",
type="string",
description="提示词名称(get_prompt 操作需要)",
required=False
),
ToolParameter(
name="prompt_arguments",
type="object",
description="提示词参数(get_prompt 操作可选)",
required=False
)
]
class A2ATool(Tool):
"""A2A (Agent-to-Agent Protocol) 工具
连接到 A2A Agent 并进行通信。
功能:
- 向 Agent 提问
- 获取 Agent 信息
- 发送自定义消息
使用示例:
>>> from hello_agents.tools.builtin import A2ATool
>>> # 连接到 A2A Agent(使用默认名称)
>>> tool = A2ATool(agent_url="http://localhost:5000")
>>> # 连接到 A2A Agent(自定义名称和描述)
>>> tool = A2ATool(
... agent_url="http://localhost:5000",
... name="tech_expert",
... description="技术专家,回答技术相关问题"
... )
>>> # 提问
>>> result = tool.run({"action": "ask", "question": "计算 2+2"})
>>> # 获取信息
>>> result = tool.run({"action": "get_info"})
注意:需要安装官方 a2a-sdk 库: pip install a2a-sdk
详见文档: docs/chapter10/A2A_GUIDE.md
官方仓库: https://github.com/a2aproject/a2a-python
"""
def __init__(self, agent_url: str, name: str = "a2a", description: str = None):
"""
初始化 A2A 工具
Args:
agent_url: Agent URL
name: 工具名称(可选,默认为 "a2a")
description: 工具描述(可选)
"""
if description is None:
description = "连接到 A2A Agent,支持提问和获取信息。需要安装官方 a2a-sdk 库。"
super().__init__(
name=name,
description=description
)
self.agent_url = agent_url
def run(self, parameters: Dict[str, Any]) -> str:
"""
执行 A2A 操作
Args:
parameters: 包含以下参数的字典
- action: 操作类型 (ask, get_info)
- question: 问题文本(ask 需要)
Returns:
操作结果
"""
try:
from helloAgents.protocols.a2a.implementation import A2AClient, A2A_AVAILABLE
if not A2A_AVAILABLE:
return ("错误:需要安装 a2a-sdk 库\n"
"安装命令: pip install a2a-sdk\n"
"详见文档: docs/chapter10/A2A_GUIDE.md\n"
"官方仓库: https://github.com/a2aproject/a2a-python")
except ImportError:
return ("错误:无法导入 A2A 模块\n"
"安装命令: pip install a2a-sdk\n"
"详见文档: docs/chapter10/A2A_GUIDE.md\n"
"官方仓库: https://github.com/a2aproject/a2a-python")
action = parameters.get("action", "").lower()
if not action:
return "错误:必须指定 action 参数"
try:
client = A2AClient(self.agent_url)
if action == "ask":
question = parameters.get("question")
if not question:
return "错误:必须指定 question 参数"
response = client.ask(question)
return f"Agent 回答:\n{response}"
elif action == "get_info":
info = client.get_info()
result = "Agent 信息:\n"
for key, value in info.items():
result += f"- {key}: {value}\n"
return result
else:
return f"错误:不支持的操作 '{action}'"
except Exception as e:
return f"A2A 操作失败: {str(e)}"
def get_parameters(self) -> List[ToolParameter]:
"""获取工具参数定义"""
return [
ToolParameter(
name="action",
type="string",
description="操作类型: ask(提问), get_info(获取信息)",
required=True
),
ToolParameter(
name="question",
type="string",
description="问题文本(ask 操作需要)",
required=False
)
]
class ANPTool(Tool):
"""ANP (Agent Network Protocol) 工具
提供智能体网络管理功能,包括服务发现、节点管理和消息路由。
这是一个概念性实现,用于演示 Agent 网络管理的核心理念。
功能:
- 注册和发现服务
- 添加和管理网络节点
- 消息路由
- 网络统计
使用示例:
>>> from hello_agents.tools.builtin import ANPTool
>>> tool = ANPTool()
>>> # 注册服务
>>> result = tool.run({
... "action": "register_service",
... "service_id": "calc-1",
... "service_type": "calculator",
... "endpoint": "http://localhost:5001"
... })
>>> # 发现服务
>>> result = tool.run({
... "action": "discover_services",
... "service_type": "calculator"
... })
>>> # 添加节点
>>> result = tool.run({
... "action": "add_node",
... "node_id": "agent-1",
... "endpoint": "http://localhost:5001"
... })
注意:这是概念性实现,不需要额外依赖
详见文档: docs/chapter10/ANP_CONCEPTS.md
"""
def __init__(self, name: str = "anp", description: str = None, discovery=None, network=None):
"""初始化 ANP 工具
Args:
name: 工具名称
description: 工具描述
discovery: 可选的 ANPDiscovery 实例,如果不提供则创建新实例
network: 可选的 ANPNetwork 实例,如果不提供则创建新实例
"""
if description is None:
description = "智能体网络管理工具,支持服务发现、节点管理和消息路由。概念性实现。"
super().__init__(
name=name,
description=description
)
from helloAgents.protocols.anp.implementation import ANPDiscovery, ANPNetwork
self._discovery = discovery if discovery is not None else ANPDiscovery()
self._network = network if network is not None else ANPNetwork()
def run(self, parameters: Dict[str, Any]) -> str:
"""
执行 ANP 操作
Args:
parameters: 包含以下参数的字典
- action: 操作类型 (register_service, discover_services, add_node, route_message, get_stats)
- service_id, service_type, endpoint: 服务信息(register_service 需要)
- node_id, endpoint: 节点信息(add_node 需要)
- from_node, to_node, message: 路由信息(route_message 需要)
Returns:
操作结果
"""
from helloAgents.protocols.anp.implementation import ServiceInfo
action = parameters.get("action", "").lower()
if not action:
return "错误:必须指定 action 参数"
try:
if action == "register_service":
service_id = parameters.get("service_id")
service_type = parameters.get("service_type")
endpoint = parameters.get("endpoint")
metadata = parameters.get("metadata", {})
if not all([service_id, service_type, endpoint]):
return "错误:必须指定 service_id, service_type 和 endpoint 参数"
service = ServiceInfo(service_id, service_type, endpoint, metadata)
self._discovery.register_service(service)
return f"✅ 已注册服务 '{service_id}'"
elif action == "unregister_service":
service_id = parameters.get("service_id")
if not service_id:
return "错误:必须指定 service_id 参数"
# 使用 ANPDiscovery 的 unregister_service 方法
success = self._discovery.unregister_service(service_id)
if success:
return f"✅ 已注销服务 '{service_id}'"
else:
return f"错误:服务 '{service_id}' 不存在"
elif action == "discover_services":
service_type = parameters.get("service_type")
services = self._discovery.discover_services(service_type)
if not services:
return "没有找到服务"
result = f"找到 {len(services)} 个服务:\n\n"
for service in services:
result += f"服务ID: {service.service_id}\n"
result += f" 名称: {service.service_name}\n"
result += f" 类型: {service.service_type}\n"
result += f" 端点: {service.endpoint}\n"
if service.capabilities:
result += f" 能力: {', '.join(service.capabilities)}\n"
if service.metadata:
result += f" 元数据: {service.metadata}\n"
result += "\n"
return result
elif action == "add_node":
node_id = parameters.get("node_id")
endpoint = parameters.get("endpoint")
metadata = parameters.get("metadata", {})
if not all([node_id, endpoint]):
return "错误:必须指定 node_id 和 endpoint 参数"
self._network.add_node(node_id, endpoint, metadata)
return f"✅ 已添加节点 '{node_id}'"
elif action == "route_message":
from_node = parameters.get("from_node")
to_node = parameters.get("to_node")
message = parameters.get("message", {})
if not all([from_node, to_node]):
return "错误:必须指定 from_node 和 to_node 参数"
path = self._network.route_message(from_node, to_node, message)
if path:
return f"消息路由路径: {' -> '.join(path)}"
else:
return "无法找到路由路径"
elif action == "get_stats":
stats = self._network.get_network_stats()
result = "网络统计:\n"
for key, value in stats.items():
result += f"- {key}: {value}\n"
return result
else:
return f"错误:不支持的操作 '{action}'"
except Exception as e:
return f"ANP 操作失败: {str(e)}"
def get_parameters(self) -> List[ToolParameter]:
"""获取工具参数定义"""
return [
ToolParameter(
name="action",
type="string",
description="操作类型: register_service, unregister_service, discover_services, add_node, route_message, get_stats",
required=True
),
ToolParameter(
name="service_id",
type="string",
description="服务 ID(register_service, unregister_service 需要)",
required=False
),
ToolParameter(
name="service_type",
type="string",
description="服务类型(register_service 需要)",
required=False
),
ToolParameter(
name="endpoint",
type="string",
description="端点地址(register_service, add_node 需要)",
required=False
),
ToolParameter(
name="node_id",
type="string",
description="节点 ID(add_node 需要)",
required=False
),
ToolParameter(
name="from_node",
type="string",
description="源节点 ID(route_message 需要)",
required=False
),
ToolParameter(
name="to_node",
type="string",
description="目标节点 ID(route_message 需要)",
required=False
),
ToolParameter(
name="message",
type="object",
description="消息内容(route_message 需要)",
required=False
),
ToolParameter(
name="metadata",
type="object",
description="元数据(register_service, add_node 可选)",
required=False
)
]