MCP AI应用通信的底层机制

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在AI应用快速发展的今天，不同AI系统之间的高效通信成为技术架构的关键挑战。MCP（Model Context Protocol）作为新一代AI应用通信协议，正在重新定义AI工具生态的构建方式。

想象一下，当你需要让ChatGPT调用外部API、访问数据库或执行复杂计算时，传统的插件机制往往面临兼容性差、扩展性有限的困境。MCP协议的出现，为这个问题提供了优雅的解决方案。通过标准化的通信接口和灵活的架构设计，MCP让AI应用能够无缝集成各种外部服务和工具，就像给AI装上了"万能工具箱"。无论是数据查询、文件处理还是复杂业务逻辑，MCP都能让AI应用以统一的方式调用和协作。

一、MCP协议概述

什么是MCP协议

MCP（Model Context Protocol）是一个开放标准协议，专门设计用于AI模型与外部工具和服务之间的通信。它采用JSON-RPC 2.0规范，通过标准化的消息格式实现AI应用与各种外部资源的无缝集成。

{"jsonrpc": "2.0","id": 1,"method": "tools/list","params": {}
}

MCP的核心思想是提供一个统一的接口层，让AI模型能够像调用本地函数一样调用远程服务，同时保持协议的简洁性和扩展性。

MCP的设计目标与核心价值

MCP的设计遵循几个核心原则：

标准化：统一的协议规范，确保不同实现之间的互操作性
安全性：内置认证和授权机制，保护敏感数据
可扩展性：支持动态工具发现和注册
性能优化：高效的序列化和传输机制

这些设计目标使得MCP成为构建AI工具生态的理想选择。

MCP在AI生态中的定位

在当前的AI技术栈中，MCP扮演着"连接器"的角色。它位于AI模型层和应用服务层之间，为两者提供标准化的通信桥梁。

AI模型层 (ChatGPT, Claude等)↓
MCP协议层 (标准化通信接口)↓
应用服务层 (数据库、API、文件系统等)

这种分层架构使得AI应用能够灵活地集成各种外部资源，而不需要为每种服务开发专门的适配器。

二、MCP的核心架构

协议层设计原理

MCP采用分层设计，每一层都有明确的职责：

传输层：处理底层网络通信，支持TCP、WebSocket等多种传输方式
协议层：实现JSON-RPC 2.0规范，处理消息的序列化和反序列化
应用层：定义具体的工具接口和业务逻辑

# MCP服务器基础实现示例
import json
import asyncioclass MCPServer:def __init__(self):self.tools = {}async def handle_request(self, request):"""处理MCP请求"""try:method = request.get("method")params = request.get("params", {})if method == "tools/list":return {"result": list(self.tools.keys())}elif method == "tools/call":tool_name = params.get("name")tool_params = params.get("arguments", {})return await self.call_tool(tool_name, tool_params)except Exception as e:return {"error": {"code": -1, "message": str(e)}}

客户端-服务器模型

MCP采用经典的客户端-服务器架构，其中：

客户端：通常是AI模型或AI应用，发起工具调用请求
服务器：提供各种工具和服务，响应客户端的请求

# MCP客户端示例
class MCPClient:def __init__(self, server_url):self.server_url = server_urlasync def list_tools(self):"""获取可用工具列表"""request = {"jsonrpc": "2.0","id": 1,"method": "tools/list","params": {}}return await self.send_request(request)async def call_tool(self, tool_name, arguments):"""调用指定工具"""request = {"jsonrpc": "2.0","id": 2,"method": "tools/call","params": {"name": tool_name,"arguments": arguments}}return await self.send_request(request)

消息传递机制

MCP使用JSON-RPC 2.0作为消息传递协议，支持三种消息类型：

请求消息：客户端向服务器发送的调用请求
响应消息：服务器返回的处理结果
通知消息：单向消息，不需要响应

// 请求消息示例
{"jsonrpc": "2.0","id": 1,"method": "tools/call","params": {"name": "database/query","arguments": {"sql": "SELECT * FROM users WHERE age > 18"}}
}// 响应消息示例
{"jsonrpc": "2.0","id": 1,"result": {"data": [{"id": 1, "name": "张三", "age": 25},{"id": 2, "name": "李四", "age": 30}]}
}

错误处理与容错机制

MCP定义了标准的错误码和错误处理机制：

# 错误处理示例
class MCPError(Exception):def __init__(self, code, message, data=None):self.code = codeself.message = messageself.data = data# 标准错误码
ERROR_CODES = {-32700: "解析错误",-32600: "无效请求",-32601: "方法未找到",-32602: "无效参数",-32603: "内部错误"
}def handle_error(error_code, message):"""统一错误处理"""return {"jsonrpc": "2.0","error": {"code": error_code,"message": message}}

三、MCP协议详解

请求-响应模式

MCP采用同步的请求-响应模式，每个请求都有唯一的ID，确保响应的正确匹配：

class RequestManager:def __init__(self):self.pending_requests = {}self.request_id = 0def create_request(self, method, params):"""创建新请求"""self.request_id += 1request = {"jsonrpc": "2.0","id": self.request_id,"method": method,"params": params}self.pending_requests[self.request_id] = requestreturn requestdef handle_response(self, response):"""处理响应"""request_id = response.get("id")if request_id in self.pending_requests:del self.pending_requests[request_id]return responsereturn None

数据格式与序列化

MCP使用JSON作为数据交换格式，支持复杂的数据结构：

# 数据类型示例
class MCPDataTypes:@staticmethoddef serialize_tool_result(data):"""序列化工具调用结果"""if isinstance(data, (dict, list)):return json.dumps(data, ensure_ascii=False)return str(data)@staticmethoddef deserialize_tool_params(params_str):"""反序列化工具参数"""try:return json.loads(params_str)except json.JSONDecodeError:raise MCPError(-32700, "参数格式错误")

认证与安全机制

MCP支持多种认证方式，确保通信安全：

# 认证机制示例
class MCPAuth:def __init__(self):self.api_keys = {}def authenticate(self, headers):"""验证请求身份"""api_key = headers.get("Authorization")if not api_key:raise MCPError(-32001, "缺少认证信息")if api_key not in self.api_keys:raise MCPError(-32002, "无效的API密钥")return Truedef add_api_key(self, key, permissions):"""添加API密钥"""self.api_keys[key] = permissions

版本兼容性策略

MCP采用语义化版本控制，确保向后兼容：

# 版本兼容性处理
class MCPVersionManager:def __init__(self):self.supported_versions = ["1.0", "1.1", "1.2"]def check_compatibility(self, client_version):"""检查版本兼容性"""major, minor = client_version.split(".")for supported in self.supported_versions:s_major, s_minor = supported.split(".")if major == s_major and int(minor) <= int(s_minor):return Truereturn Falsedef negotiate_version(self, client_versions):"""协商最佳版本"""for version in client_versions:if version in self.supported_versions:return versionreturn self.supported_versions[0]

四、MCP的实际应用场景

AI应用集成外部API

MCP最常见的应用场景是让AI模型调用外部API服务：

# 天气API集成示例
class WeatherTool:def __init__(self, api_key):self.api_key = api_keyasync def get_weather(self, city):"""获取城市天气信息"""url = f"https://api.weatherapi.com/v1/current.json"params = {"key": self.api_key,"q": city,"aqi": "no"}async with aiohttp.ClientSession() as session:async with session.get(url, params=params) as response:data = await response.json()return {"city": city,"temperature": data["current"]["temp_c"],"condition": data["current"]["condition"]["text"]}# 注册到MCP服务器
mcp_server = MCPServer()
weather_tool = WeatherTool("your_api_key")
mcp_server.tools["weather/get"] = weather_tool.get_weather

数据库连接与查询

MCP可以安全地让AI模型访问数据库：

# 数据库工具示例
class DatabaseTool:def __init__(self, connection_string):self.connection_string = connection_stringasync def query_database(self, sql, params=None):"""执行数据库查询"""try:async with asyncpg.connect(self.connection_string) as conn:result = await conn.fetch(sql, *params or [])return [dict(row) for row in result]except Exception as e:raise MCPError(-32603, f"数据库查询错误: {str(e)}")async def execute_sql(self, sql, params=None):"""执行SQL语句"""try:async with asyncpg.connect(self.connection_string) as conn:result = await conn.execute(sql, *params or [])return {"affected_rows": result}except Exception as e:raise MCPError(-32603, f"SQL执行错误: {str(e)}")# 注册数据库工具
db_tool = DatabaseTool("postgresql://user:pass@localhost/db")
mcp_server.tools["database/query"] = db_tool.query_database
mcp_server.tools["database/execute"] = db_tool.execute_sql

文件系统操作

MCP支持安全的文件系统操作：

# 文件系统工具示例
class FileSystemTool:def __init__(self, base_path):self.base_path = Path(base_path)async def read_file(self, file_path):"""读取文件内容"""full_path = self.base_path / file_pathif not full_path.exists():raise MCPError(-32003, "文件不存在")try:with open(full_path, 'r', encoding='utf-8') as f:return {"content": f.read(), "path": str(full_path)}except Exception as e:raise MCPError(-32603, f"文件读取错误: {str(e)}")async def write_file(self, file_path, content):"""写入文件内容"""full_path = self.base_path / file_pathtry:full_path.parent.mkdir(parents=True, exist_ok=True)with open(full_path, 'w', encoding='utf-8') as f:f.write(content)return {"success": True, "path": str(full_path)}except Exception as e:raise MCPError(-32603, f"文件写入错误: {str(e)}")# 注册文件系统工具
fs_tool = FileSystemTool("/safe/path")
mcp_server.tools["filesystem/read"] = fs_tool.read_file
mcp_server.tools["filesystem/write"] = fs_tool.write_file

自定义工具开发

开发者可以创建自定义工具来扩展AI的能力：

# 自定义计算工具示例
class CalculatorTool:def __init__(self):self.supported_operations = ['+', '-', '*', '/', '**']async def calculate(self, expression):"""安全计算数学表达式"""try:# 安全检查，只允许基本数学运算allowed_chars = set('0123456789+-*/.() ')if not all(c in allowed_chars for c in expression):raise ValueError("表达式包含不允许的字符")result = eval(expression)return {"expression": expression, "result": result}except Exception as e:raise MCPError(-32602, f"计算错误: {str(e)}")async def get_statistics(self, numbers):"""计算统计信息"""try:nums = [float(n) for n in numbers]return {"mean": sum(nums) / len(nums),"max": max(nums),"min": min(nums),"count": len(nums)}except Exception as e:raise MCPError(-32602, f"统计计算错误: {str(e)}")# 注册计算工具
calc_tool = CalculatorTool()
mcp_server.tools["calculator/compute"] = calc_tool.calculate
mcp_server.tools["calculator/stats"] = calc_tool.get_statistics

五、MCP与其他协议的对比

与OpenAI插件协议的差异

MCP相比OpenAI插件协议具有以下优势：

标准化程度更高：MCP是开放标准，不依赖特定厂商
扩展性更强：支持动态工具发现和注册
安全性更好：内置认证和授权机制

# OpenAI插件 vs MCP对比示例
# OpenAI插件方式
class OpenAIPlugin:def __init__(self):self.manifest = {"schema_version": "v1","name_for_model": "weather_plugin","name_for_human": "天气查询插件","description_for_model": "获取城市天气信息","description_for_human": "查询指定城市的天气情况","auth": {"type": "none"},"api": {"type": "openapi", "url": "https://api.example.com/openapi.yaml"},"logo_url": "https://example.com/logo.png","contact_email": "support@example.com","legal_info_url": "https://example.com/legal"}# MCP方式
class MCPTool:def __init__(self):self.tool_info = {"name": "weather/get","description": "获取城市天气信息","parameters": {"type": "object","properties": {"city": {"type": "string", "description": "城市名称"}},"required": ["city"]}}

与REST API的对比分析

MCP相比传统REST API的优势：

统一接口：所有工具通过相同的协议调用
类型安全：支持强类型参数定义
错误处理：标准化的错误码和错误信息

# REST API vs MCP对比
# 传统REST API调用
async def rest_api_call():async with aiohttp.ClientSession() as session:# 需要为每个API维护不同的调用方式weather_response = await session.get("https://api.weather.com/current?city=北京")db_response = await session.post("https://api.database.com/query", json={"sql": "SELECT * FROM users"})file_response = await session.get("https://api.files.com/read?path=/data.txt")# MCP统一调用
async def mcp_call():client = MCPClient("mcp://localhost:3000")# 所有工具使用相同的调用方式weather = await client.call_tool("weather/get", {"city": "北京"})users = await client.call_tool("database/query", {"sql": "SELECT * FROM users"})content = await client.call_tool("filesystem/read", {"path": "/data.txt"})

性能与扩展性优势

MCP在性能和扩展性方面的优势：

# 性能对比示例
import time
import asyncioclass PerformanceTest:def __init__(self):self.mcp_client = MCPClient("mcp://localhost:3000")async def test_mcp_performance(self):"""测试MCP性能"""start_time = time.time()# 并发调用多个工具tasks = [self.mcp_client.call_tool("weather/get", {"city": "北京"}),self.mcp_client.call_tool("database/query", {"sql": "SELECT * FROM users"}),self.mcp_client.call_tool("calculator/compute", {"expression": "2+2*3"})]results = await asyncio.gather(*tasks)end_time = time.time()return {"total_time": end_time - start_time,"results": results}async def test_scalability(self):"""测试扩展性"""# 动态添加新工具new_tool = {"name": "custom/process","description": "自定义数据处理工具","handler": lambda data: {"processed": data.upper()}}# 无需重启服务器即可使用新工具result = await self.mcp_client.call_tool("custom/process", {"data": "hello world"})return result

六、MCP开发实践指南

环境搭建与配置

开始MCP开发需要搭建基础环境：

# 环境配置示例
# requirements.txt
mcp==1.0.0
aiohttp==3.8.5
asyncpg==0.28.0
pydantic==2.0.0# 基础配置文件
import yamlclass MCPConfig:def __init__(self, config_path="mcp_config.yaml"):with open(config_path, 'r', encoding='utf-8') as f:self.config = yaml.safe_load(f)@propertydef server_host(self):return self.config.get("server", {}).get("host", "localhost")@propertydef server_port(self):return self.config.get("server", {}).get("port", 3000)@propertydef auth_token(self):return self.config.get("auth", {}).get("token")@propertydef tools_config(self):return self.config.get("tools", {})# mcp_config.yaml
server:host: localhostport: 3000debug: trueauth:token: "your_secret_token"enabled: truetools:weather:api_key: "your_weather_api_key"enabled: truedatabase:connection_string: "postgresql://user:pass@localhost/db"enabled: truefilesystem:base_path: "/safe/path"enabled: true

基础客户端开发

开发MCP客户端的基本步骤：

# 基础MCP客户端实现
import asyncio
import aiohttp
import json
from typing import Dict, Anyclass MCPClient:def __init__(self, server_url: str, auth_token: str = None):self.server_url = server_urlself.auth_token = auth_tokenself.session = Noneself.request_id = 0async def __aenter__(self):"""异步上下文管理器入口"""self.session = aiohttp.ClientSession()return selfasync def __aexit__(self, exc_type, exc_val, exc_tb):"""异步上下文管理器出口"""if self.session:await self.session.close()async def send_request(self, method: str, params: Dict[str, Any] = None) -> Dict[str, Any]:"""发送MCP请求"""self.request_id += 1request = {"jsonrpc": "2.0","id": self.request_id,"method": method,"params": params or {}}headers = {"Content-Type": "application/json"}if self.auth_token:headers["Authorization"] = f"Bearer {self.auth_token}"async with self.session.post(self.server_url,json=request,headers=headers) as response:result = await response.json()if "error" in result:raise MCPError(result["error"]["code"],result["error"]["message"])return result["result"]async def list_tools(self) -> list:"""获取可用工具列表"""return await self.send_request("tools/list")async def call_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:"""调用指定工具"""return await self.send_request("tools/call", {"name": tool_name,"arguments": arguments})# 使用示例
async def main():async with MCPClient("http://localhost:3000", "your_token") as client:# 获取可用工具tools = await client.list_tools()print(f"可用工具: {tools}")# 调用天气工具weather = await client.call_tool("weather/get", {"city": "北京"})print(f"天气信息: {weather}")# 调用数据库工具users = await client.call_tool("database/query", {"sql": "SELECT * FROM users LIMIT 5"})print(f"用户数据: {users}")if __name__ == "__main__":asyncio.run(main())

自定义工具实现

创建自定义MCP工具的完整示例：

# 自定义工具实现
from typing import Dict, Any, Optional
import asyncio
import jsonclass CustomTool:"""自定义MCP工具基类"""def __init__(self, name: str, description: str):self.name = nameself.description = descriptionself.parameters = {}def add_parameter(self, name: str, param_type: str, description: str, required: bool = False):"""添加工具参数"""self.parameters[name] = {"type": param_type,"description": description,"required": required}async def execute(self, arguments: Dict[str, Any]) -> Dict[str, Any]:"""执行工具逻辑（子类需要重写）"""raise NotImplementedError("子类必须实现execute方法")def get_info(self) -> Dict[str, Any]:"""获取工具信息"""return {"name": self.name,"description": self.description,"parameters": {"type": "object","properties": self.parameters,"required": [name for name, param in self.parameters.items() if param.get("required", False)]}}# 图像处理工具示例
class ImageProcessingTool(CustomTool):def __init__(self):super().__init__("image/process", "图像处理工具")self.add_parameter("image_url", "string", "图像URL", required=True)self.add_parameter("operation", "string", "处理操作(resize/rotate/filter)", required=True)self.add_parameter("parameters", "object", "处理参数")async def execute(self, arguments: Dict[str, Any]) -> Dict[str, Any]:"""执行图像处理"""image_url = arguments["image_url"]operation = arguments["operation"]params = arguments.get("parameters", {})# 模拟图像处理if operation == "resize":width = params.get("width", 800)height = params.get("height", 600)return {"status": "success","operation": "resize","new_size": f"{width}x{height}","processed_url": f"{image_url}_resized"}elif operation == "rotate":angle = params.get("angle", 90)return {"status": "success","operation": "rotate","angle": angle,"processed_url": f"{image_url}_rotated"}else:raise MCPError(-32602, f"不支持的操作: {operation}")# 数据分析工具示例
class DataAnalysisTool(CustomTool):def __init__(self):super().__init__("data/analyze", "数据分析工具")self.add_parameter("data", "array", "要分析的数据", required=True)self.add_parameter("analysis_type", "string", "分析类型(statistics/trend/correlation)", required=True)async def execute(self, arguments: Dict[str, Any]) -> Dict[str, Any]:"""执行数据分析"""data = arguments["data"]analysis_type = arguments["analysis_type"]if not isinstance(data, list):raise MCPError(-32602, "数据必须是数组格式")if analysis_type == "statistics":return self._calculate_statistics(data)elif analysis_type == "trend":return self._analyze_trend(data)elif analysis_type == "correlation":return self._calculate_correlation(data)else:raise MCPError(-32602, f"不支持的分析类型: {analysis_type}")def _calculate_statistics(self, data: list) -> Dict[str, Any]:"""计算统计信息"""if not data:return {"error": "数据为空"}numeric_data = [float(x) for x in data if isinstance(x, (int, float))]if not numeric_data:return {"error": "没有有效的数值数据"}return {"count": len(numeric_data),"sum": sum(numeric_data),"mean": sum(numeric_data) / len(numeric_data),"min": min(numeric_data),"max": max(numeric_data),"median": sorted(numeric_data)[len(numeric_data) // 2]}def _analyze_trend(self, data: list) -> Dict[str, Any]:"""分析趋势"""if len(data) < 2:return {"error": "数据点不足，无法分析趋势"}numeric_data = [float(x) for x in data if isinstance(x, (int, float))]if len(numeric_data) < 2:return {"error": "数值数据点不足"}# 简单趋势分析first_half = numeric_data[:len(numeric_data)//2]second_half = numeric_data[len(numeric_data)//2:]first_avg = sum(first_half) / len(first_half)second_avg = sum(second_half) / len(second_half)trend = "上升" if second_avg > first_avg else "下降" if second_avg < first_avg else "平稳"return {"trend": trend,"change_rate": (second_avg - first_avg) / first_avg * 100,"first_half_avg": first_avg,"second_half_avg": second_avg}def _calculate_correlation(self, data: list) -> Dict[str, Any]:"""计算相关性（简化版）"""if len(data) < 4:return {"error": "数据点不足，无法计算相关性"}# 假设数据是成对的if len(data) % 2 != 0:return {"error": "数据必须是成对的"}pairs = [(data[i], data[i+1]) for i in range(0, len(data), 2)]x_values = [pair[0] for pair in pairs if isinstance(pair[0], (int, float))]y_values = [pair[1] for pair in pairs if isinstance(pair[1], (int, float))]if len(x_values) != len(y_values) or len(x_values) < 2:return {"error": "有效的数据对不足"}# 简化的相关系数计算n = len(x_values)sum_x = sum(x_values)sum_y = sum(y_values)sum_xy = sum(x * y for x, y in zip(x_values, y_values))sum_x2 = sum(x * x for x in x_values)sum_y2 = sum(y * y for y in y_values)numerator = n * sum_xy - sum_x * sum_ydenominator = ((n * sum_x2 - sum_x * sum_x) * (n * sum_y2 - sum_y * sum_y)) ** 0.5if denominator == 0:correlation = 0else:correlation = numerator / denominatorreturn {"correlation_coefficient": correlation,"data_points": n,"interpretation": "强正相关" if correlation > 0.7 else "中等正相关" if correlation > 0.3 else"弱正相关" if correlation > 0 else"强负相关" if correlation < -0.7 else"中等负相关" if correlation < -0.3 else"弱负相关" if correlation < 0 else "无相关"}# 注册自定义工具
def register_custom_tools(mcp_server: MCPServer):"""注册自定义工具到MCP服务器"""# 注册图像处理工具image_tool = ImageProcessingTool()mcp_server.tools[image_tool.name] = image_tool.execute# 注册数据分析工具data_tool = DataAnalysisTool()mcp_server.tools[data_tool.name] = data_tool.executereturn mcp_server

调试与测试技巧

MCP开发的调试和测试策略：

# 调试和测试工具
import logging
import unittest
from unittest.mock import Mock, patchclass MCPDebugger:def __init__(self):self.logger = logging.getLogger("mcp_debugger")self.logger.setLevel(logging.DEBUG)# 添加控制台处理器handler = logging.StreamHandler()formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')handler.setFormatter(formatter)self.logger.addHandler(handler)def log_request(self, request: Dict[str, Any]):"""记录请求日志"""self.logger.info(f"发送请求: {json.dumps(request, ensure_ascii=False, indent=2)}")def log_response(self, response: Dict[str, Any]):"""记录响应日志"""self.logger.info(f"收到响应: {json.dumps(response, ensure_ascii=False, indent=2)}")def log_error(self, error: Exception):"""记录错误日志"""self.logger.error(f"发生错误: {str(error)}", exc_info=True)# MCP测试框架
class MCPTestCase(unittest.TestCase):def setUp(self):"""测试设置"""self.mcp_server = MCPServer()self.mcp_client = MCPClient("http://localhost:3000")self.debugger = MCPDebugger()async def test_tool_registration(self):"""测试工具注册"""# 注册测试工具async def test_tool(arguments):return {"result": "test_success", "input": arguments}self.mcp_server.tools["test/tool"] = test_tool# 验证工具列表tools = await self.mcp_client.list_tools()self.assertIn("test/tool", tools)async def test_tool_execution(self):"""测试工具执行"""# 注册测试工具async def calculator(arguments):a = arguments.get("a", 0)b = arguments.get("b", 0)operation = arguments.get("operation", "+")if operation == "+":return {"result": a + b}elif operation == "-":return {"result": a - b}elif operation == "*":return {"result": a * b}elif operation == "/":if b == 0:raise MCPError(-32602, "除数不能为零")return {"result": a / b}else:raise MCPError(-32602, f"不支持的操作: {operation}")self.mcp_server.tools["calculator/compute"] = calculator# 测试各种运算test_cases = [({"a": 10, "b": 5, "operation": "+"}, 15),({"a": 10, "b": 5, "operation": "-"}, 5),({"a": 10, "b": 5, "operation": "*"}, 50),({"a": 10, "b": 5, "operation": "/"}, 2),]for args, expected in test_cases:result = await self.mcp_client.call_tool("calculator/compute", args)self.assertEqual(result["result"], expected)async def test_error_handling(self):"""测试错误处理"""# 注册会抛出错误的工具async def error_tool(arguments):raise MCPError(-32602, "测试错误")self.mcp_server.tools["test/error"] = error_tool# 测试错误处理with self.assertRaises(MCPError) as context:await self.mcp_client.call_tool("test/error", {})self.assertEqual(context.exception.code, -32602)self.assertEqual(context.exception.message, "测试错误")# 性能测试
class MCPPerformanceTest:def __init__(self):self.mcp_client = MCPClient("http://localhost:3000")async def benchmark_tool_calls(self, tool_name: str, arguments: Dict[str, Any], iterations: int = 100):"""基准测试工具调用性能"""import timestart_time = time.time()# 并发执行多次调用tasks = [self.mcp_client.call_tool(tool_name, arguments)for _ in range(iterations)]results = await asyncio.gather(*tasks)end_time = time.time()total_time = end_time - start_timeavg_time = total_time / iterationsreturn {"total_time": total_time,"iterations": iterations,"average_time": avg_time,"throughput": iterations / total_time,"success_rate": len([r for r in results if "error" not in r]) / len(results)}async def stress_test(self, concurrent_requests: int = 50):"""压力测试"""async def make_request(request_id: int):try:result = await self.mcp_client.call_tool("test/stress", {"id": request_id})return {"id": request_id, "status": "success", "result": result}except Exception as e:return {"id": request_id, "status": "error", "error": str(e)}tasks = [make_request(i) for i in range(concurrent_requests)]results = await asyncio.gather(*tasks)success_count = len([r for r in results if r["status"] == "success"])error_count = len([r for r in results if r["status"] == "error"])return {"total_requests": concurrent_requests,"successful_requests": success_count,"failed_requests": error_count,"success_rate": success_count / concurrent_requests}# 使用示例
async def run_tests():"""运行所有测试"""# 单元测试test_case = MCPTestCase()await test_case.test_tool_registration()await test_case.test_tool_execution()await test_case.test_error_handling()# 性能测试perf_test = MCPPerformanceTest()benchmark_result = await perf_test.benchmark_tool_calls("calculator/compute", {"a": 10, "b": 5, "operation": "+"}, 100)print(f"性能测试结果: {benchmark_result}")stress_result = await perf_test.stress_test(50)print(f"压力测试结果: {stress_result}")if __name__ == "__main__":asyncio.run(run_tests())

通过以上完整的MCP开发指南，你可以看到MCP协议不仅是一个技术标准，更是一个完整的生态系统。它通过标准化的接口、灵活的架构设计和丰富的工具生态，为AI应用开发提供了强大的基础设施支持。

无论是简单的API调用，还是复杂的业务逻辑处理，MCP都能提供统一、安全、高效的解决方案。随着AI技术的不断发展，MCP协议将继续演进，为构建更智能、更强大的AI应用生态系统贡献力量。