MCP（Model Context Protocol）应用功能全解析：从原理到实践的系统化教程

# MCP请求结构示例
class MCPRequest:
    def __init__(self):
        self.session_id = ""  # 会话标识符
        self.context = {}     # 上下文信息
        self.metadata = {}    # 元数据
        self.functions = []   # 可用函数列表
        self.messages = []    # 消息历史

# 上下文注入示例
def inject_context(request, additional_context):
    # 合并基础上下文和附加上下文
    request.context.update(additional_context)
    # 确保上下文格式符合规范
    validate_context(request.context)
    return request

# 工具函数注册示例
from mcp_sdk import register_tool

@register_tool
class WeatherTool:
    name = "get_weather"
    description = "获取指定城市的天气预报"
    parameters = {
        "type": "object",
        "properties": {
            "city": {"type": "string", "description": "城市名称"},
            "days": {"type": "integer", "description": "预报天数"}
        },
        "required": ["city"]
    }
    
    def execute(self, params):
        city = params["city"]
        days = params.get("days", 1)
        # 实现获取天气的逻辑
        return {"city": city, "weather": "sunny", "temperature": 25}

# 权限声明示例
@register_tool(required_permissions=["file:read", "network:external"])
class DocumentAnalyzer:
    # 工具实现...
    pass

# 自定义语义指令绑定示例
from mcp_nlu import register_semantic_pattern

@register_semantic_pattern(pattern=r"查找关于(.*)的信息")
def search_information(match_groups):
    topic = match_groups[0]
    return {"action": "search", "topic": topic}

# 知识库问答系统示例
from mcp_rag import KnowledgeBase, SemanticSearch, ContextInjector

# 初始化知识库
kb = KnowledgeBase(
    documents_path="./company_docs",
    embedding_model="text-embedding-ada-002"
)

# 构建向量索引
kb.build_index()

# 语义搜索组件
search = SemanticSearch(knowledge_base=kb, top_k=3)

# 上下文注入器
injector = ContextInjector()

# 处理用户查询的流程
def process_query(query):
    # 1. 语义搜索相关文档
    relevant_docs = search.search(query)
    
    # 2. 构建检索到的上下文
    retrieved_context = "\n".join([doc.content for doc in relevant_docs])
    
    # 3. 构建完整请求（包含用户查询和检索上下文）
    request = MCPRequest(
        messages=[{"role": "user", "content": query}],
        context={"retrieved_knowledge": retrieved_context}
    )
    
    # 4. 发送请求到MCP处理
    response = mcp_client.process(request)
    
    return response.content

# 数据库交互工具
@register_tool
class DatabaseTool:
    name = "query_database"
    description = "执行SQL查询并返回结果"
    parameters = {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "SQL查询语句"}
        },
        "required": ["query"]
    }
    
    def execute(self, params):
        query = params["query"]
        # 执行查询并返回结构化结果
        with sqlite3.connect("company_data.db") as conn:
            cursor = conn.cursor()
            cursor.execute(query)
            columns = [desc[0] for desc in cursor.description]
            rows = cursor.fetchall()
            return {
                "columns": columns,
                "rows": rows
            }

# 图像分析工具
@register_tool
class ImageAnalyzer:
    name = "analyze_image"
    description = "分析图像内容并返回详细描述"
    parameters = {
        "type": "object",
        "properties": {
            "image_path": {"type": "string", "description": "图像文件路径"},
            "analysis_type": {"type": "string", "description": "分析类型：'caption'、'objects'、'text'、'all'"}
        },
        "required": ["image_path"]
    }
    
    def execute(self, params):
        image_path = params["image_path"]
        analysis_type = params.get("analysis_type", "all")
        
        results = {}
        
        if analysis_type in ["caption", "all"]:
            # 生成图像描述
            results["caption"] = generate_image_caption(image_path)
        
        if analysis_type in ["objects", "all"]:
            # 检测图像中的物体
            results["objects"] = detect_objects(image_path)
        
        if analysis_type in ["text", "all"]:
            # 提取图像中的文本
            results["text"] = extract_text_from_image(image_path)
        
        return results

# 多模态处理请求
class MultimodalRequest(MCPRequest):
    def __init__(self):
        super().__init__()
        self.image_inputs = []  # 图像输入
        self.audio_inputs = []  # 音频输入
        self.text_inputs = []   # 文本输入

# 处理多模态请求的函数
def process_multimodal(request):
    # 1. 提取各模态输入
    text = " ".join(request.text_inputs)
    
    # 2. 分析图像内容
    image_results = []
    for img_path in request.image_inputs:
        analyzer = ImageAnalyzer()
        image_results.append(analyzer.execute({"image_path": img_path}))
    
    # 3. 分析音频内容
    audio_results = []
    for audio_path in request.audio_inputs:
        # 音频处理逻辑
        audio_results.append(process_audio(audio_path))
    
    # 4. 构建融合上下文
    fusion_context = {
        "text": text,
        "images": image_results,
        "audios": audio_results
    }
    
    # 5. 发送到多模态模型处理
    # ...

# 完整的工具函数注册与调用示例
from mcp_sdk import register_tool, MCPClient

# 1. 定义天气查询工具
@register_tool
class WeatherTool:
    name = "get_weather"
    description = "获取指定城市的天气预报"
    parameters = {
        "type": "object",
        "properties": {
            "city": {"type": "string", "description": "城市名称"},
            "date": {"type": "string", "description": "日期 (YYYY-MM-DD)"}
        },
        "required": ["city"]
    }
    
    def execute(self, params):
        city = params["city"]
        date = params.get("date", "今天")
        # 模拟获取天气数据
        return {
            "city": city,
            "date": date,
            "temperature": 25,
            "weather": "晴",
            "humidity": 60,
            "wind": "3-4级"
        }

# 2. 定义计算器工具
@register_tool
class CalculatorTool:
    name = "calculate"
    description = "执行数学计算"
    parameters = {
        "type": "object",
        "properties": {
            "expression": {"type": "string", "description": "数学表达式"}
        },
        "required": ["expression"]
    }
    
    def execute(self, params):
        expression = params["expression"]
        try:
            # 注意：实际生产环境需要更安全的计算方式
            result = eval(expression)
            return {"expression": expression, "result": result}
        except Exception as e:
            return {"error": str(e)}

# 3. 初始化MCP客户端
client = MCPClient()

# 4. 注册工具
client.register_tools([WeatherTool(), CalculatorTool()])

# 5. 发送包含工具调用的请求
response = client.chat_completion({
    "messages": [
        {"role": "user", "content": "北京明天的天气怎么样？气温是多少度？"}
    ],
    "tools": client.available_tools
})

# 6. 处理工具调用响应
if response.tool_calls:
    # 执行工具调用
    tool_results = []
    for tool_call in response.tool_calls:
        if tool_call.function.name == "get_weather":
            tool = WeatherTool()
            result = tool.execute(tool_call.function.arguments)
            tool_results.append({
                "tool_call_id": tool_call.id,
                "name": tool.name,
                "result": result
            })
    
    # 将工具执行结果发送回模型
    final_response = client.chat_completion({
        "messages": [
            {"role": "user", "content": "北京明天的天气怎么样？气温是多少度？"},
            {"role": "assistant", "tool_calls": response.tool_calls},
            {"role": "tool", "tool_results": tool_results}
        ]
    })
    
    print(final_response.content)

2. **工作流编排与自动化**

工作流编排与自动化是MCP的高级功能，它允许将多个工具调用和处理步骤组织成复杂的工作流，实现更复杂的任务自动化。

**主要特点**：
- **支持条件分支**：根据中间结果动态决定执行路径
- **循环控制**：支持迭代处理数据集合
- **异常处理**：完善的错误捕获和处理机制
- **声明式和命令式定义**：支持多种工作流定义方式
- **事件驱动架构**：基于事件触发工作流执行

**工作流自动化示例**：

```python
# 数据处理自动化工作流示例
from mcp_workflow import Workflow, Step, Condition

class DataProcessingWorkflow(Workflow):
    name = "data_processing_pipeline"
    description = "自动从数据源获取数据，进行清洗、分析并生成报告"
    
    def define_workflow(self):
        # 步骤1: 数据获取
        step_fetch = Step(
            name="fetch_data",
            tool="fetch_from_database",  # 假设已注册的数据获取工具
            parameters={
                "connection_string": "${workflow.params.db_connection}",
                "query": "${workflow.params.query}"
            },
            output_key="raw_data"
        )
        
        # 步骤2: 数据清洗
        step_clean = Step(
            name="clean_data",
            tool="data_cleaner",  # 假设已注册的数据清洗工具
            parameters={
                "data": "${steps.fetch_data.output}",
                "operations": ["remove_duplicates", "fill_missing_values"]
            },
            output_key="cleaned_data"
        )
        
        # 条件: 检查数据量是否足够
        condition_data_sufficient = Condition(
            name="check_data_volume",
            expression="len(steps.clean_data.output) > 100"
        )
        
        # 条件分支1: 数据量充足时的分析步骤
        step_analyze_full = Step(
            name="analyze_full_data",
            tool="comprehensive_analyzer",
            parameters={"data": "${steps.clean_data.output}"},
            output_key="analysis_result"
        )
        
        # 条件分支2: 数据量不足时的简化分析
        step_analyze_simple = Step(
            name="analyze_simple_data",
            tool="basic_analyzer",
            parameters={"data": "${steps.clean_data.output}"},
            output_key="analysis_result"
        )
        
        # 步骤3: 生成报告
        step_generate_report = Step(
            name="generate_report",
            tool="report_generator",
            parameters={
                "analysis_result": "${steps.analysis_result.output}",
                "format": "pdf",
                "title": "${workflow.params.report_title}"
            },
            output_key="report"
        )
        
        # 步骤4: 发送通知
        step_notify = Step(
            name="send_notification",
            tool="email_sender",
            parameters={
                "recipient": "${workflow.params.recipient_email}",
                "subject": "报告已生成: ${workflow.params.report_title}",
                "body": "您的数据报告已准备就绪，请查收附件。",
                "attachment": "${steps.generate_report.output.file_path}"
            }
        )
        
        # 定义工作流结构
        workflow_structure = [
            step_fetch,
            step_clean,
            [
                condition_data_sufficient,
                [step_analyze_full],  # 条件为真时执行
                [step_analyze_simple]  # 条件为假时执行
            ],
            step_generate_report,
            step_notify
        ]
        
        return workflow_structure

# 工作流注册与执行示例
def run_data_processing_pipeline():
    # 注册工作流
    workflow_registry = WorkflowRegistry()
    workflow_registry.register_workflow(DataProcessingWorkflow())
    
    # 执行工作流
    executor = WorkflowExecutor(registry=workflow_registry)
    execution_id = executor.execute_workflow(
        workflow_name="data_processing_pipeline",
        params={
            "db_connection": "postgresql://user:password@localhost:5432/mydatabase",
            "query": "SELECT * FROM sales_data WHERE date > '2024-01-01'",
            "report_title": "2024年销售数据分析报告",
            "recipient_email": "manager@company.com"
        }
    )
    
    # 监控工作流执行状态
    try:
        # 等待工作流完成
        result = executor.wait_for_completion(execution_id, timeout=3600)
        print(f"工作流执行成功: {result.status}")
        print(f"报告路径: {result.outputs['report']['file_path']}")
    except TimeoutError:
        print("工作流执行超时")
    except Exception as e:
        print(f"监控工作流时发生错误: {str(e)}")

# Ollama本地模型集成示例
from mcp_local import LocalModelClient, ModelConfig

# 配置Ollama本地模型
ollama_config = ModelConfig(
    provider="ollama",
    model="llama3:8b-instruct",
    base_url="http://localhost:11434/api",
    context_size=4096,
    temperature=0.7
)

# 创建本地模型客户端
local_client = LocalModelClient(config=ollama_config)

# 测试本地模型响应
def test_local_model():
    response = local_client.chat_completion({
        "messages": [
            {"role": "user", "content": "请简要介绍MCP协议的核心概念"}
        ],
        "max_tokens": 500
    })
    
    print("本地模型响应:")
    print(response.content)
    
    # 检查模型性能指标
    metrics = local_client.get_performance_metrics()
    print(f"\n性能指标:")
    print(f"- 推理延迟: {metrics['inference_latency_ms']}ms")
    print(f"- 内存使用: {metrics['memory_usage_mb']}MB")
    print(f"- GPU利用率: {metrics.get('gpu_utilization', 'N/A')}%")

# 智能资源调度示例
from mcp_scheduler import ResourceScheduler, DevicePolicy

# 创建资源调度器
class SmartScheduler(ResourceScheduler):
    def __init__(self):
        super().__init__()
        self.cpu_only_models = ["llama3:8b", "mistral:7b"]
        self.gpu_optimized_models = ["llama3:70b", "gemma:27b"]
    
    def decide_device(self, model_name, task_complexity, system_status):
        """根据模型、任务复杂度和系统状态决定使用的计算设备"""
        # 高复杂度任务优先使用GPU
        if task_complexity > 0.7:
            if system_status.gpu_available and model_name in self.gpu_optimized_models:
                return DevicePolicy.GPU
        
        # 检查GPU内存是否充足
        if system_status.gpu_memory_available > 10 * 1024:  # 至少10GB可用
            return DevicePolicy.GPU
        
        # 否则使用CPU
        return DevicePolicy.CPU
    
    def optimize_model(self, model_name, device_policy):
        """根据目标设备优化模型"""
        optimization_params = {}
        
        if device_policy == DevicePolicy.CPU:
            # CPU模式下使用INT8量化
            optimization_params["quantization"] = "int8"
            optimization_params["num_threads"] = 8
        else:
            # GPU模式下使用FP16
            optimization_params["precision"] = "fp16"
            optimization_params["tensor_split"] = [0.5, 0.5]  # 多GPU分割
        
        return optimization_params

# 使用智能调度器
def setup_smart_inference():
    scheduler = SmartScheduler()
    
    # 注册模型
    scheduler.register_model(
        name="llama3:8b",
        path="./models/llama3_8b",
        default_device=DevicePolicy.CPU
    )
    
    scheduler.register_model(
        name="llama3:70b",
        path="./models/llama3_70b",
        default_device=DevicePolicy.GPU
    )
    
    # 配置自动卸载策略（内存不足时）
    scheduler.set_auto_unload_policy(
        memory_threshold=85,  # 内存使用率超过85%时
        priority_based=True   # 基于使用优先级
    )
    
    return scheduler

# 企业知识管理系统示例
from mcp_core import MCPServer, Router
from mcp_rag import KnowledgeBaseManager
from mcp_authentication import AuthMiddleware

class EnterpriseKnowledgeSystem:
    def __init__(self):
        # 初始化MCP服务器
        self.server = MCPServer(
            host="0.0.0.0",
            port=8080,
            middleware=[AuthMiddleware()]
        )
        
        # 初始化知识库管理器
        self.kb_manager = KnowledgeBaseManager(
            embedding_model="text-embedding-3-large",
            vector_db="milvus",
            chunk_size=1000,
            chunk_overlap=200
        )
        
        # 注册文档处理工具
        self.register_tools()
        
        # 设置路由
        self.setup_routes()
    
    def register_tools(self):
        """注册系统所需的工具函数"""
        # 文档上传工具
        @self.server.register_tool
        class DocumentUploader:
            name = "upload_document"
            description = "上传文档到企业知识库"
            # 工具实现...
        
        # 知识检索工具
        @self.server.register_tool
        class KnowledgeRetriever:
            name = "retrieve_knowledge"
            description = "从知识库检索相关信息"
            # 工具实现...
        
        # 知识图谱构建工具
        @self.server.register_tool
        class KnowledgeGraphBuilder:
            name = "build_knowledge_graph"
            description = "基于文档内容构建知识图谱"
            # 工具实现...
    
    def setup_routes(self):
        """设置API路由"""
        router = Router()
        
        @router.post("/api/query")
        async def handle_query(request):
            # 处理用户查询请求
            query = request.json.get("query")
            user_context = request.json.get("context", {})
            
            # 1. 检索相关知识
            retrieved_docs = self.kb_manager.retrieve_relevant_docs(
                query=query,
                user_profile=user_context.get("user_profile"),
                top_k=5
            )
            
            # 2. 构建增强上下文
            enhanced_context = {
                "query": query,
                "retrieved_knowledge": retrieved_docs,
                "user_context": user_context
            }
            
            # 3. 调用模型生成回答
            response = await self.server.generate_response(
                prompt="基于检索到的知识回答用户问题",
                context=enhanced_context
            )
            
            # 4. 记录交互历史
            self.kb_manager.log_interaction(query, response, retrieved_docs)
            
            return {"answer": response, "sources": retrieved_docs}
        
        # 注册路由
        self.server.add_router(router)
    
    def start(self):
        """启动服务"""
        print("企业知识管理系统启动中...")
        self.server.start()

# 启动系统
system = EnterpriseKnowledgeSystem()
system.start()

# 智能客服系统核心实现
class IntelligentCustomerService:
    def __init__(self):
        # 初始化MCP客户端
        self.mcp_client = MCPClient(model="gpt-4-turbo")
        
        # 注册渠道适配器
        self.channel_adapters = {
            "web": WebChannelAdapter(),
            "app": MobileAppChannelAdapter(),
            "wechat": WechatChannelAdapter(),
            "phone": PhoneChannelAdapter(),
            "email": EmailChannelAdapter()
        }
        
        # 注册客服工具
        self.register_service_tools()
        
        # 初始化对话管理器
        self.dialog_manager = DialogManager()
    
    def register_service_tools(self):
        """注册客服系统所需工具"""
        # 订单查询工具
        @self.mcp_client.register_tool
        class OrderInquiryTool:
            name = "query_order"
            description = "查询用户订单信息"
            parameters = {
                "type": "object",
                "properties": {
                    "order_id": {"type": "string", "description": "订单ID"},
                    "user_id": {"type": "string", "description": "用户ID"}
                },
                "required": []
            }
            
            def execute(self, params):
                order_id = params.get("order_id")
                user_id = params.get("user_id")
                # 查询订单系统
                return order_system.query(order_id=order_id, user_id=user_id)
        
        # 工单创建工具
        @self.mcp_client.register_tool
        class TicketCreationTool:
            name = "create_ticket"
            description = "创建服务工单"
            parameters = {
                "type": "object",
                "properties": {
                    "user_id": {"type": "string", "description": "用户ID"},
                    "category": {"type": "string", "description": "工单类别"},
                    "description": {"type": "string", "description": "问题描述"}
                },
                "required": ["user_id", "category", "description"]
            }
            
            def execute(self, params):
                # 创建工单
                return ticket_system.create(params)
        
        # 产品信息查询工具
        @self.mcp_client.register_tool
        class ProductInfoTool:
            name = "query_product"
            description = "查询产品信息"
            parameters = {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string", "description": "产品ID"},
                    "product_name": {"type": "string", "description": "产品名称"}
                },
                "required": []
            }
            
            def execute(self, params):
                # 查询产品信息
                return product_system.query(**params)
    
    async def handle_message(self, channel, user_id, message, context=None):
        """处理用户消息"""
        context = context or {}
        
        # 获取渠道适配器
        adapter = self.channel_adapters.get(channel)
        if not adapter:
            return {"error": "不支持的渠道"}
        
        # 预处理消息
        processed_message = adapter.preprocess_message(message)
        
        # 获取用户上下文
        user_context = await self.dialog_manager.get_user_context(user_id)
        
        # 构建MCP请求
        request = {
            "messages": [
                {"role": "system", "content": "你是一位专业的客服代表，负责解答用户问题。"},
                {"role": "user", "content": processed_message}
            ],
            "context": {
                "user_context": user_context,
                "channel": channel,
                **context
            },
            "tools": self.mcp_client.available_tools
        }
        
        # 发送请求并获取响应
        response = await self.mcp_client.chat_completion(request)
        
        # 处理工具调用
        if response.tool_calls:
            tool_results = []
            for tool_call in response.tool_calls:
                # 执行工具调用
                result = await self.execute_tool_call(tool_call)
                tool_results.append({
                    "tool_call_id": tool_call.id,
                    "name": tool_call.function.name,
                    "result": result
                })
            
            # 将工具结果发送回模型
            request["messages"].append({"role": "assistant", "tool_calls": response.tool_calls})
            request["messages"].append({"role": "tool", "tool_results": tool_results})
            final_response = await self.mcp_client.chat_completion(request)
        else:
            final_response = response
        
        # 后处理响应
        formatted_response = adapter.format_response(final_response.content)
        
        # 更新对话历史
        await self.dialog_manager.update_conversation(
            user_id=user_id,
            user_message=processed_message,
            assistant_message=final_response.content,
            channel=channel
        )
        
        return {"response": formatted_response}
    
    async def execute_tool_call(self, tool_call):
        """执行工具调用"""
        tool_name = tool_call.function.name
        tool_args = tool_call.function.arguments
        
        # 获取注册的工具
        tool = self.mcp_client.get_tool(tool_name)
        if not tool:
            return {"error": f"未知工具: {tool_name}"}
        
        # 执行工具
        return tool.execute(tool_args)

# 智能内容创作助手
class ContentCreationAssistant:
    def __init__(self):
        # 初始化MCP客户端
        self.mcp_client = MCPClient(model="claude-3-opus")
        
        # 注册创作工具
        self.register_creation_tools()
        
        # 初始化资源管理器
        self.resource_manager = ResourceManager()
    
    def register_creation_tools(self):
        """注册创作相关工具"""
        # 内容生成工具
        @self.mcp_client.register_tool
        class ContentGeneratorTool:
            name = "generate_content"
            description = "根据主题和要求生成内容"
            parameters = {
                "type": "object",
                "properties": {
                    "topic": {"type": "string", "description": "内容主题"},
                    "style": {"type": "string", "description": "内容风格"},
                    "length": {"type": "integer", "description": "内容长度（字数）"},
                    "structure": {"type": "string", "description": "内容结构"}
                },
                "required": ["topic"]
            }
            
            def execute(self, params):
                # 生成内容
                return content_engine.generate(**params)
        
        # SEO分析工具
        @self.mcp_client.register_tool
        class SEOAnalyzerTool:
            name = "analyze_seo"
            description = "分析内容的SEO优化情况"
            parameters = {
                "type": "object",
                "properties": {
                    "content": {"type": "string", "description": "待分析内容"},
                    "keywords": {"type": "array", "items": {"type": "string"}, "description": "目标关键词"}
                },
                "required": ["content"]
            }
            
            def execute(self, params):
                # 分析SEO
                return seo_analyzer.analyze(**params)
        
        # 图片生成工具
        @self.mcp_client.register_tool
        class ImageGenerationTool:
            name = "generate_image"
            description = "根据文本描述生成图片"
            parameters = {
                "type": "object",
                "properties": {
                    "prompt": {"type": "string", "description": "图片描述"},
                    "style": {"type": "string", "description": "图片风格"},
                    "aspect_ratio": {"type": "string", "description": "宽高比"}
                },
                "required": ["prompt"]
            }
            
            def execute(self, params):
                # 生成图片
                return image_generator.generate(**params)
        
        # 多语言翻译工具
        @self.mcp_client.register_tool
        class TranslationTool:
            name = "translate_content"
            description = "将内容翻译成其他语言"
            parameters = {
                "type": "object",
                "properties": {
                    "content": {"type": "string", "description": "待翻译内容"},
                    "target_language": {"type": "string", "description": "目标语言"},
                    "preserve_formatting": {"type": "boolean", "description": "是否保留格式"}
                },
                "required": ["content", "target_language"]
            }
            
            def execute(self, params):
                # 翻译内容
                return translator.translate(**params)
    
    async def assist_creation(self, user_input, context=None):
        """辅助内容创作"""
        context = context or {}
        
        # 构建MCP请求
        request = {
            "messages": [
                {"role": "system", "content": "你是一位专业的内容创作助手，负责帮助用户创建高质量内容。"},
                {"role": "user", "content": user_input}
            ],
            "context": context,
            "tools": self.mcp_client.available_tools
        }
        
        # 发送请求并获取响应
        response = await self.mcp_client.chat_completion(request)
        
        # 处理工具调用
        if response.tool_calls:
            tool_results = []
            for tool_call in response.tool_calls:
                # 执行工具调用
                result = await self.execute_tool_call(tool_call)
                tool_results.append({
                    "tool_call_id": tool_call.id,
                    "name": tool_call.function.name,
                    "result": result
                })
            
            # 将工具结果发送回模型
            request["messages"].append({"role": "assistant", "tool_calls": response.tool_calls})
            request["messages"].append({"role": "tool", "tool_results": tool_results})
            final_response = await self.mcp_client.chat_completion(request)
        else:
            final_response = response
        
        return final_response.content
    
    async def execute_tool_call(self, tool_call):
        """执行工具调用"""
        tool_name = tool_call.function.name
        tool_args = tool_call.function.arguments
        
        # 获取注册的工具
        tool = self.mcp_client.get_tool(tool_name)
        if not tool:
            return {"error": f"未知工具: {tool_name}"}
        
        # 执行工具
        return tool.execute(tool_args)

# Kubernetes部署配置示例
# mcp_k8s_deployment.py
from kubernetes import client, config

class MCPK8sDeployer:
    def __init__(self):
        # 加载Kubernetes配置
        config.load_kube_config()
        self.apps_v1 = client.AppsV1Api()
        self.core_v1 = client.CoreV1Api()
    
    def deploy_mcp_server(self, namespace="mcp-system"):
        """部署MCP服务器"""
        # 创建部署
        deployment = client.V1Deployment(
            api_version="apps/v1",
            kind="Deployment",
            metadata=client.V1ObjectMeta(name="mcp-server"),
            spec=client.V1DeploymentSpec(
                replicas=3,
                selector=client.V1LabelSelector(
                    match_labels={"app": "mcp-server"}
                ),
                template=client.V1PodTemplateSpec(
                    metadata=client.V1ObjectMeta(
                        labels={"app": "mcp-server"}
                    ),
                    spec=client.V1PodSpec(
                        containers=[
                            client.V1Container(
                                name="mcp-server",
                                image="mcp/server:latest",
                                ports=[client.V1ContainerPort(container_port=8080)],
                                resources=client.V1ResourceRequirements(
                                    requests={"cpu": "500m", "memory": "1Gi"},
                                    limits={"cpu": "1", "memory": "2Gi"}
                                ),
                                env=[
                                    client.V1EnvVar(name="MCP_MODEL_ENDPOINT", value="http://model-service:8000/v1/chat/completions"),
                                    client.V1EnvVar(name="MCP_STORAGE_BACKEND", value="s3"),
                                    client.V1EnvVar(name="MCP_LOG_LEVEL", value="info")
                                ],
                                readiness_probe=client.V1Probe(
                                    http_get=client.V1HTTPGetAction(
                                        path="/health",
                                        port=8080
                                    )
                                )
                            )
                        ]
                    )
                )
            )
        )
        
        # 创建服务
        service = client.V1Service(
            api_version="v1",
            kind="Service",
            metadata=client.V1ObjectMeta(name="mcp-server"),
            spec=client.V1ServiceSpec(
                selector={"app": "mcp-server"},
                ports=[client.V1ServicePort(port=80, target_port=8080)],
                type="ClusterIP"
            )
        )
        
        # 创建命名空间（如果不存在）
        try:
            self.core_v1.read_namespace(namespace)
        except:
            namespace_obj = client.V1Namespace(
                metadata=client.V1ObjectMeta(name=namespace)
            )
            self.core_v1.create_namespace(namespace_obj)
        
        # 部署资源
        self.apps_v1.create_namespaced_deployment(namespace=namespace, body=deployment)
        self.core_v1.create_namespaced_service(namespace=namespace, body=service)
        
        print(f"MCP服务器已部署到命名空间 {namespace}")
    
    def deploy_model_service(self, namespace="mcp-system"):
        """部署模型推理服务"""
        # 实现模型服务部署逻辑
        pass
    
    def setup_ingress(self, namespace="mcp-system"):
        """配置入口路由"""
        # 实现Ingress配置逻辑
        pass

# 使用示例
deployer = MCPK8sDeployer()
deployer.deploy_mcp_server()

# 低代码平台MCP连接器示例
class MCPConnector:
    def __init__(self, platform):
        self.platform = platform
        self.mcp_client = MCPClient(base_url="http://mcp-server:8080")
        
        # 注册MCP组件到低代码平台
        self.register_components()
    
    def register_components(self):
        """注册MCP组件到低代码平台"""
        # 注册AI对话组件
        @self.platform.register_component
        class AIChatComponent:
            name = "AI对话组件"
            description = "与AI模型进行对话交互"
            icon = "💬"
            
            properties = {
                "model": {
                    "type": "select",
                    "label": "选择模型",
                    "options": ["gpt-4", "claude-3", "llama-3"],
                    "default": "gpt-4"
                },
                "system_prompt": {
                    "type": "textarea",
                    "label": "系统提示词",
                    "default": "你是一位专业的助手。"
                },
                "temperature": {
                    "type": "slider",
                    "label": "温度参数",
                    "min": 0,
                    "max": 1,
                    "default": 0.7
                }
            }
            
            events = ["onResponse", "onError"]
            
            async def execute(self, props, events):
                try:
                    # 调用MCP服务
                    response = await self.mcp_client.chat_completion({
                        "model": props["model"],
                        "messages": [
                            {"role": "system", "content": props["system_prompt"]},
                            {"role": "user", "content": props["user_input"]}
                        ],
                        "temperature": props["temperature"]
                    })
                    
                    # 触发成功事件
                    events["onResponse"](response.content)
                    return response.content
                except Exception as e:
                    # 触发错误事件
                    events["onError"](str(e))
                    return None
        
        # 注册文档处理组件
        @self.platform.register_component
        class DocumentProcessorComponent:
            name = "文档处理组件"
            description = "智能处理文档内容"
            icon = "📄"
            
            # 组件属性和实现...
            
            async def execute(self, props, events):
                # 实现文档处理逻辑
                pass
        
        # 注册工具调用组件
        @self.platform.register_component
        class ToolCallComponent:
            name = "工具调用组件"
            description = "调用MCP注册的工具函数"
            icon = "🔧"
            
            # 组件属性和实现...
            
            async def execute(self, props, events):
                # 实现工具调用逻辑
                pass
    
    def create_workflow_template(self):
        """创建工作流模板"""
        # 创建并返回预设的工作流模板
        pass

# 使用示例
connector = MCPConnector(lowcode_platform)

# 边缘MCP实现示例
class EdgeMCP:
    def __init__(self, device_id):
        self.device_id = device_id
        self.local_models = {}
        self.cloud_client = CloudMCPClient(api_key="your_api_key")
        
        # 初始化边缘模型
        self.initialize_models()
        
        # 配置本地存储
        self.storage = EdgeStorage()
    
    def initialize_models(self):
        """初始化边缘推理模型"""
        # 加载轻量级模型
        try:
            import onnxruntime as ort
            
            # 加载本地ONNX模型
            self.local_models["text_classification"] = ort.InferenceSession(
                "./models/text_classifier.onnx"
            )
            
            self.local_models["object_detection"] = ort.InferenceSession(
                "./models/object_detector.onnx"
            )
            
            # 加载嵌入式LLM
            self.local_models["mini_llm"] = self._load_quantized_model(
                "./models/llama3_8b_quantized.onnx"
            )
            
        except Exception as e:
            print(f"加载边缘模型失败: {e}")
    
    async def process_request(self, request):
        """处理边缘请求"""
        # 确定处理策略
        processing_strategy = self._determine_strategy(request)
        
        if processing_strategy == "local":
            # 本地处理
            result = await self._process_locally(request)
            
            # 异步同步到云端
            self._sync_to_cloud(request, result, False)
            
            return result
            
        elif processing_strategy == "cloud":
            # 云端处理
            return await self.cloud_client.process_request(request)
            
        elif processing_strategy == "hybrid":
            # 混合处理
            local_result = await self._process_locally(request)
            cloud_result = await self.cloud_client.process_request(request)
            
            # 合并结果
            return self._merge_results(local_result, cloud_result)
    
    def _determine_strategy(self, request):
        """根据请求类型和设备状态确定处理策略"""
        # 实现策略决策逻辑
        # 考虑因素：网络状态、电池电量、计算资源、请求复杂度等
        pass
    
    async def _process_locally(self, request):
        """本地处理请求"""
        # 根据请求类型调用相应的本地模型
        if request.get("type") == "text":
            return self._process_text_locally(request)
        elif request.get("type") == "image":
            return self._process_image_locally(request)
        elif request.get("type") == "sensor":
            return self._process_sensor_data(request)
        else:
            return {"error": "不支持的请求类型"}
    
    def _sync_to_cloud(self, request, result, retry=True):
        """同步请求和结果到云端"""
        # 实现异步同步逻辑
        pass

# 使用示例
edge_mcp = EdgeMCP(device_id="edge_gateway_001")

# 多模态MCP接口示例
class MultimodalMCPClient:
    def __init__(self, base_url="http://mcp-server:8080"):
        self.base_url = base_url
        self.session = requests.Session()
    
    async def process_multimodal(self, request):
        """处理多模态请求"""
        # 构建请求
        payload = {
            "model": request.get("model", "multimodal-llm"),
            "messages": request.get("messages", []),
            "media": request.get("media", []),
            "context": request.get("context", {})
        }
        
        # 准备文件数据
        files = {}
        for i, media in enumerate(request.get("media", [])):
            if media.get("type") == "image":
                files[f"image_{i}"] = (
                    media.get("filename", f"image_{i}.jpg"),
                    open(media.get("path"), "rb"),
                    media.get("content_type", "image/jpeg")
                )
            elif media.get("type") == "audio":
                files[f"audio_{i}"] = (
                    media.get("filename", f"audio_{i}.wav"),
                    open(media.get("path"), "rb"),
                    media.get("content_type", "audio/wav")
                )
        
        # 发送请求
        response = await self.session.post(
            f"{self.base_url}/api/multimodal",
            json=payload,
            files=files if files else None
        )
        
        return response.json()
    
    async def create_agent_session(self, config):
        """创建支持多模态的智能体会话"""
        # 实现智能体会话创建逻辑
        pass

# MCP自适应优化器
class AdaptiveOptimizer:
    def __init__(self, mcp_server):
        self.mcp_server = mcp_server
        self.metrics_collector = MetricsCollector()
        self.decision_engine = DecisionEngine()
        self.optimizer = OptimizationScheduler()
        
        # 启动优化循环
        self.optimizer.start(self._optimize_loop)
    
    async def _optimize_loop(self):
        """优化循环"""
        while True:
            # 1. 收集指标
            metrics = await self.metrics_collector.collect()
            
            # 2. 分析指标
            analysis = await self.decision_engine.analyze(metrics)
            
            # 3. 执行优化
            if analysis["needs_optimization"]:
                actions = analysis["recommended_actions"]
                for action in actions:
                    await self._execute_optimization(action)
            
            # 4. 等待下一个优化周期
            await asyncio.sleep(analysis["next_check_interval"])  
    
    async def _execute_optimization(self, action):
        """执行优化操作"""
        action_type = action.get("type")
        
        if action_type == "scale_model":
            # 调整模型实例数量
            await self.mcp_server.scale_model(
                model_id=action["model_id"],
                replicas=action["replicas"]
            )
            
        elif action_type == "tune_parameters":
            # 调整模型参数
            await self.mcp_server.update_model_config(
                model_id=action["model_id"],
                parameters=action["parameters"]
            )
            
        elif action_type == "cache_policy":
            # 更新缓存策略
            await self.mcp_server.update_cache_policy(
                policy=action["policy"]
            )
            
        elif action_type == "route_adjustment":
            # 调整请求路由
            await self.mcp_server.update_route_rules(
                rules=action["rules"]
            )