client.py•5.08 kB
import asyncio
import os
import json
from typing import Optional
from contextlib import AsyncExitStack
from openai import OpenAI
from dotenv import load_dotenv
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
# 加载 .env 文件，确保 API Key 受到保护
load_dotenv()
class MCPClient:
def __init__(self):
"""初始化 MCP 客户端"""
self.exit_stack = AsyncExitStack()
self.openai_api_key = os.getenv("OPENAI_API_KEY") # 读取 OpenAI API Key
self.base_url = os.getenv("BASE_URL") # 读取 BASE URL
self.model = os.getenv("MODEL") # 读取 model
if not self.openai_api_key:
raise ValueError("❌ 未找到 OpenAI API Key，请在 .env 文件中设置OPENAI_API_KEY")
self.client = OpenAI(api_key=self.openai_api_key, base_url=self.base_url)
# 创建OpenAI client
self.session: Optional[ClientSession] = None
async def connect_to_server(self, server_script_path: str):
"""连接到 MCP 服务器并列出可用工具"""
is_python = server_script_path.endswith('.py')
is_js = server_script_path.endswith('.js')
if not (is_python or is_js):
raise ValueError("服务器脚本必须是 .py 或 .js 文件")
command = "python" if is_python else "node"
server_params = StdioServerParameters(
command=command,
args=[server_script_path],
env=None
)
# 启动 MCP 服务器并建立通信
stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
self.stdio, self.write = stdio_transport
self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))
await self.session.initialize()
# 列出 MCP 服务器上的工具
response = await self.session.list_tools()
tools = response.tools
print("\n已连接到服务器，支持以下工具:", [tool.name for tool in tools])
async def process_query(self, query: str) -> str:
"""
使用大模型处理查询并调用可用的 MCP 工具 (Function Calling)
"""
messages = [{"role": "user", "content": query}]
response = await self.session.list_tools()
available_tools = [{
"type": "function",
"function": {
"name": tool.name,
"description": tool.description,
"input_schema": tool.inputSchema
}
} for tool in response.tools]
# print(available_tools)
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
tools=available_tools
)
# 处理返回的内容
content = response.choices[0]
# print(f"TEST: {content}")
if hasattr(content, 'finish_reason') and content.finish_reason == "tool_calls":
# 如果是需要使用工具，就解析工具
tool_call = content.message.tool_calls[0]
tool_name = tool_call.function.name
tool_args = json.loads(tool_call.function.arguments)
# 执行工具
result = await self.session.call_tool(tool_name, tool_args)
print(f"\n\n[Calling tool {tool_name} with args {tool_args}]\n\n")
# 将模型返回的调用哪个工具数据和工具执行完成后的数据都存入messages中
messages.append(content.message.model_dump())
messages.append({
"role": "tool",
"content": result.content[0].text,
"tool_call_id": tool_call.id,
})
# 将上面的结果再返回给大模型用于生产最终的结果
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
)
return response.choices[0].message.content
return content.message.content
async def chat_loop(self):
"""运行交互式聊天循环"""
print("\n🤖 MCP 客户端已启动！输入 'quit' 退出")
while True:
try:
query = input("\n你: ").strip()
if query.lower() == 'quit':
break
response = await self.process_query(query) # 发送用户输入到 OpenAI API
print(f"\n🤖 OpenAI: {response}")
except Exception as e:
print(f"\n⚠️ 发生错误: {str(e)}")
async def cleanup(self):
"""清理资源"""
await self.exit_stack.aclose()
async def main():
import sys
if len(sys.argv) < 2:
print("Usage: python client.py <path_to_server_script>")
sys.exit(1)
client = MCPClient()
try:
await client.connect_to_server(sys.argv[1])
await client.chat_loop()
finally:
await client.cleanup()
if __name__ == "__main__":
import sys
asyncio.run(main())