LangBot中的MCP协议支持

摘要

MCP（Model Context Protocol）是一种新兴的标准化协议，旨在为大语言模型应用提供统一的上下文管理和工具调用接口。LangBot作为先进的聊天机器人框架，率先支持MCP协议，使得开发者能够通过标准化的方式扩展模型能力。本文将深入探讨LangBot中MCP协议的实现机制，包括协议集成、工具映射、上下文管理等方面，并通过实际示例展示如何在LangBot中使用MCP协议扩展聊天机器人的功能。

正文

1. MCP协议概述

MCP（Model Context Protocol）是由ModelContextProtocol.io组织制定的开放协议，旨在解决大语言模型应用中的上下文管理和工具调用标准化问题。MCP协议的主要特点包括：

• 标准化接口：提供统一的API接口，屏蔽不同工具和服务的差异
• 上下文管理：支持结构化的上下文数据管理
• 工具调用：定义标准的工具调用机制
• 资源访问：提供标准化的资源访问接口
• 可扩展性：支持自定义扩展和插件机制

2. 系统架构

LangBot中MCP协议支持的架构如下图所示：

3. 核心组件

3.1 MCP服务管理器

MCP服务管理器是LangBot中MCP协议支持的核心组件，负责管理MCP服务器连接和协议交互：

class MCPManager:
    """MCP服务管理器"""
    
    def __init__(self, ap: app.Application):
        self.ap = ap
        self.servers: dict[str, MCPServer] = {}
        self.tools: dict[str, MCPTool] = {}
        self.resources: dict[str, MCPResource] = {}
    
    async def initialize(self):
        """初始化MCP管理器"""
        # 加载配置的MCP服务器
        await self.load_mcp_servers()
        
        # 初始化已连接的服务器
        for server in self.servers.values():
            await server.initialize()
    
    async def load_mcp_servers(self):
        """加载MCP服务器配置"""
        # 从配置文件或数据库加载MCP服务器信息
        mcp_config = self.ap.instance_config.data.get("mcp", {})
        servers = mcp_config.get("servers", [])
        
        for server_config in servers:
            server = MCPServer(
                uuid=server_config["uuid"],
                name=server_config["name"],
                endpoint=server_config["endpoint"],
                auth=server_config.get("auth", {})
            )
            self.servers[server.uuid] = server
    
    async def register_mcp_server(self, server_config: dict) -> str:
        """
        注册MCP服务器
        
        Args:
            server_config: 服务器配置
            
        Returns:
            服务器UUID
        """
        server = MCPServer(
            uuid=server_config.get("uuid") or str(uuid.uuid4()),
            name=server_config["name"],
            endpoint=server_config["endpoint"],
            auth=server_config.get("auth", {})
        )
        
        self.servers[server.uuid] = server
        await server.initialize()
        
        return server.uuid
    
    async def get_tools(self, server_uuid: str = None) -> list[MCPTool]:
        """
        获取MCP工具列表
        
        Args:
            server_uuid: 服务器UUID（可选）
            
        Returns:
            工具列表
        """
        if server_uuid:
            server = self.servers.get(server_uuid)
            if server:
                return await server.list_tools()
            return []
        else:
            # 获取所有服务器的工具
            all_tools = []
            for server in self.servers.values():
                tools = await server.list_tools()
                all_tools.extend(tools)
            return all_tools

3.2 MCP服务器实现

class MCPServer:
    """MCP服务器"""
    
    def __init__(self, uuid: str, name: str, endpoint: str, auth: dict):
        self.uuid = uuid
        self.name = name
        self.endpoint = endpoint
        self.auth = auth
        self.client: MCPClient = None
        self.connected = False
    
    async def initialize(self):
        """初始化服务器连接"""
        try:
            # 创建MCP客户端
            self.client = MCPClient(
                endpoint=self.endpoint,
                auth=self.auth
            )
            
            # 建立连接
            await self.client.connect()
            self.connected = True
            
            self.ap.logger.info(f"MCP服务器 {self.name} 连接成功")
        except Exception as e:
            self.ap.logger.error(f"MCP服务器 {self.name} 连接失败: {e}")
    
    async def list_tools(self) -> list[MCPTool]:
        """
        列出服务器提供的工具
        
        Returns:
            工具列表
        """
        if not self.connected or not self.client:
            return []
        
        try:
            tools_data = await self.client.list_tools()
            tools = [MCPTool.model_validate(tool_data) for tool_data in tools_data]
            return tools
        except Exception as e:
            self.ap.logger.error(f"获取MCP服务器 {self.name} 工具列表失败: {e}")
            return []
    
    async def call_tool(self, tool_name: str, arguments: dict) -> dict:
        """
        调用工具
        
        Args:
            tool_name: 工具名称
            arguments: 工具参数
            
        Returns:
            调用结果
        """
        if not self.connected or not self.client:
            raise RuntimeError("MCP服务器未连接")
        
        try:
            result = await self.client.call_tool(tool_name, arguments)
            return result
        except Exception as e:
            self.ap.logger.error(f"调用MCP工具 {tool_name} 失败: {e}")
            raise

4. MCP协议适配

LangBot通过协议适配器实现与MCP协议的对接：

4.1 MCP客户端实现

class MCPClient:
    """MCP客户端"""
    
    def __init__(self, endpoint: str, auth: dict):
        self.endpoint = endpoint
        self.auth = auth
        self.session: aiohttp.ClientSession = None
        self.request_id = 0
    
    async def connect(self):
        """建立连接"""
        self.session = aiohttp.ClientSession()
        
        # 如果需要认证，执行认证流程
        if self.auth:
            await self._authenticate()
    
    async def _authenticate(self):
        """执行认证"""
        auth_type = self.auth.get("type", "bearer")
        if auth_type == "bearer":
            # Bearer token认证
            token = self.auth.get("token")
            if token:
                self.session.headers["Authorization"] = f"Bearer {token}"
        elif auth_type == "basic":
            # Basic认证
            username = self.auth.get("username")
            password = self.auth.get("password")
            if username and password:
                import base64
                credentials = base64.b64encode(f"{username}:{password}".encode()).decode()
                self.session.headers["Authorization"] = f"Basic {credentials}"
    
    async def list_tools(self) -> list[dict]:
        """
        列出工具
        
        Returns:
            工具列表
        """
        self.request_id += 1
        request = {
            "jsonrpc": "2.0",
            "id": self.request_id,
            "method": "tools/list",
            "params": {}
        }
        
        async with self.session.post(self.endpoint, json=request) as response:
            if response.status == 200:
                result = await response.json()
                return result.get("result", [])
            else:
                raise RuntimeError(f"HTTP {response.status}: {await response.text()}")
    
    async def call_tool(self, tool_name: str, arguments: dict) -> dict:
        """
        调用工具
        
        Args:
            tool_name: 工具名称
            arguments: 工具参数
            
        Returns:
            调用结果
        """
        self.request_id += 1
        request = {
            "jsonrpc": "2.0",
            "id": self.request_id,
            "method": "tools/call",
            "params": {
                "name": tool_name,
                "arguments": arguments
            }
        }
        
        async with self.session.post(self.endpoint, json=request) as response:
            if response.status == 200:
                result = await response.json()
                return result.get("result", {})
            else:
                raise RuntimeError(f"HTTP {response.status}: {await response.text()}")

4.2 MCP工具映射

class MCPToolMapper:
    """MCP工具映射器"""
    
    def __init__(self, mcp_mgr: MCPManager):
        self.mcp_mgr = mcp_mgr
    
    def map_mcp_tool_to_llm_tool(self, mcp_tool: MCPTool) -> LLMTool:
        """
        将MCP工具映射为LLM工具
        
        Args:
            mcp_tool: MCP工具
            
        Returns:
            LLM工具
        """
        return LLMTool(
            name=f"mcp_{mcp_tool.name}",
            description=mcp_tool.description,
            parameters=mcp_tool.inputSchema,
            metadata={
                "source": "mcp",
                "mcp_server": mcp_tool.server_uuid,
                "mcp_tool_name": mcp_tool.name
            }
        )
    
    async def execute_mcp_tool(self, tool_name: str, parameters: dict) -> dict:
        """
        执行MCP工具
        
        Args:
            tool_name: 工具名称（映射后的名称）
            parameters: 参数
            
        Returns:
            执行结果
        """
        # 从工具名称中提取MCP服务器UUID和原始工具名称
        if not tool_name.startswith("mcp_"):
            raise ValueError("不是有效的MCP工具名称")
        
        # 查找对应的MCP工具
        for server in self.mcp_mgr.servers.values():
            tools = await server.list_tools()
            for tool in tools:
                if f"mcp_{tool.name}" == tool_name:
                    # 调用MCP工具
                    return await server.call_tool(tool.name, parameters)
        
        raise ValueError(f"MCP工具 {tool_name} 未找到")

5. 在LangBot中使用MCP

5.1 配置MCP服务器

# config.yaml
mcp:
  servers:
    - uuid: "dify-server-1"
      name: "Dify MCP Server"
      endpoint: "http://localhost:8000/mcp"
      auth:
        type: "bearer"
        token: "your-mcp-token"
    
    - uuid: "custom-server-1"
      name: "Custom MCP Server"
      endpoint: "http://custom-server:5000/mcp"
      auth:
        type: "basic"
        username: "mcp_user"
        password: "mcp_password"

5.2 MCP工具调用阶段

@stage.stage_class("mcp-tool-call")
class MCPToolCallStage(stage.PipelineStage):
    """MCP工具调用阶段"""
    
    async def initialize(self, pipeline_config: dict):
        """初始化阶段"""
        self.mcp_mapper = MCPToolMapper(self.ap.mcp_mgr)
    
    async def process(
        self,
        query: pipeline_query.Query,
        stage_inst_name: str,
    ) -> entities.StageProcessResult:
        """处理消息"""
        # 获取绑定到此流水线的MCP服务器
        bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', [])
        
        # 获取MCP工具
        mcp_tools = []
        for server_uuid in bound_mcp_servers:
            server_tools = await self.ap.mcp_mgr.get_tools(server_uuid)
            mcp_tools.extend(server_tools)
        
        if not mcp_tools:
            return entities.StageProcessResult(
                result_type=entities.ResultType.CONTINUE,
                new_query=query
            )
        
        # 将MCP工具映射为LLM工具
        llm_tools = [self.mcp_mapper.map_mcp_tool_to_llm_tool(tool) for tool in mcp_tools]
        
        # 构造消息历史
        messages = [
            {"role": "user", "content": query.message_chain.get_text()}
        ]
        
        # 调用支持工具的模型
        default_model = await self.ap.model_mgr.get_default_model()
        response = await default_model.requester.invoke_llm(
            query=query,
            model=default_model,
            messages=messages,
            funcs=llm_tools
        )
        
        # 检查是否有工具调用
        if hasattr(response, 'tool_calls') and response.tool_calls:
            # 处理工具调用
            tool_results = await self._handle_mcp_tool_calls(response.tool_calls)
            
            # 将工具调用结果添加到消息历史
            messages.append({
                "role": "assistant",
                "tool_calls": response.tool_calls
            })
            
            for result in tool_results:
                messages.append({
                    "role": "tool",
                    "tool_call_id": result["tool_call_id"],
                    "name": result["name"],
                    "content": result.get("content", result.get("error", ""))
                })
            
            # 基于工具调用结果生成最终回复
            final_response = await default_model.requester.invoke_llm(
                query=query,
                model=default_model,
                messages=messages
            )
            
            # 构造回复
            reply = platform_message.MessageChain([
                platform_message.Plain(text=final_response.content)
            ])
            
            return entities.StageProcessResult(
                result_type=entities.ResultType.CONTINUE,
                new_query=query,
                user_notice=reply,
                console_notice=f"执行了{len(tool_results)}个MCP工具调用"
            )
        
        return entities.StageProcessResult(
            result_type=entities.ResultType.CONTINUE,
            new_query=query,
            user_notice=platform_message.MessageChain([
                platform_message.Plain(text=response.content)
            ])
        )
    
    async def _handle_mcp_tool_calls(self, tool_calls: list) -> list[dict]:
        """
        处理MCP工具调用
        
        Args:
            tool_calls: 工具调用列表
            
        Returns:
            工具调用结果列表
        """
        results = []
        
        for tool_call in tool_calls:
            try:
                # 解析参数
                arguments = json.loads(tool_call.function.arguments)
                
                # 执行MCP工具
                result = await self.mcp_mapper.execute_mcp_tool(
                    tool_call.function.name,
                    arguments
                )
                
                results.append({
                    "tool_call_id": tool_call.id,
                    "name": tool_call.function.name,
                    "content": json.dumps(result, ensure_ascii=False)
                })
            except Exception as e:
                results.append({
                    "tool_call_id": tool_call.id,
                    "name": tool_call.function.name,
                    "error": f"工具执行失败: {str(e)}"
                })
        
        return results

6. MCP资源管理

MCP协议还支持资源管理，LangBot也提供了相应的支持：

class MCPResourceManager:
    """MCP资源管理器"""
    
    def __init__(self, mcp_mgr: MCPManager):
        self.mcp_mgr = mcp_mgr
    
    async def list_resources(self, server_uuid: str) -> list[dict]:
        """
        列出资源
        
        Args:
            server_uuid: 服务器UUID
            
        Returns:
            资源列表
        """
        server = self.mcp_mgr.servers.get(server_uuid)
        if not server:
            raise ValueError(f"MCP服务器 {server_uuid} 未找到")
        
        try:
            resources = await server.client.list_resources()
            return resources
        except Exception as e:
            self.ap.logger.error(f"获取MCP服务器 {server_uuid} 资源列表失败: {e}")
            return []
    
    async def read_resource(self, server_uuid: str, uri: str) -> dict:
        """
        读取资源
        
        Args:
            server_uuid: 服务器UUID
            uri: 资源URI
            
        Returns:
            资源内容
        """
        server = self.mcp_mgr.servers.get(server_uuid)
        if not server:
            raise ValueError(f"MCP服务器 {server_uuid} 未找到")
        
        try:
            content = await server.client.read_resource(uri)
            return content
        except Exception as e:
            self.ap.logger.error(f"读取MCP资源 {uri} 失败: {e}")
            raise

7. MCP协议扩展

LangBot支持通过插件扩展MCP协议功能：

class MCPExtension:
    """MCP扩展"""
    
    def __init__(self, ap: app.Application):
        self.ap = ap
    
    async def register_custom_methods(self, server: MCPServer):
        """
        注册自定义方法
        
        Args:
            server: MCP服务器
        """
        # 注册自定义方法处理器
        server.client.register_method_handler(
            "langbot/get_session_info",
            self._handle_get_session_info
        )
        
        server.client.register_method_handler(
            "langbot/send_message",
            self._handle_send_message
        )
    
    async def _handle_get_session_info(self, params: dict) -> dict:
        """
        处理获取会话信息请求
        
        Args:
            params: 参数
            
        Returns:
            会话信息
        """
        session_id = params.get("session_id")
        if not session_id:
            raise ValueError("缺少session_id参数")
        
        # 获取会话信息
        # 注意：这里需要实现具体的会话信息获取逻辑
        session_info = {
            "session_id": session_id,
            "created_at": datetime.now().isoformat(),
            "message_count": 10  # 示例数据
        }
        
        return session_info
    
    async def _handle_send_message(self, params: dict) -> dict:
        """
        处理发送消息请求
        
        Args:
            params: 参数
            
        Returns:
            发送结果
        """
        target_type = params.get("target_type")
        target_id = params.get("target_id")
        message = params.get("message")
        
        if not all([target_type, target_id, message]):
            raise ValueError("缺少必要参数")
        
        # 发送消息
        # 注意：这里需要实现具体的消息发送逻辑
        try:
            # await self.ap.platform_mgr.send_message(target_type, target_id, message)
            return {"success": True, "message_id": str(uuid.uuid4())}
        except Exception as e:
            return {"success": False, "error": str(e)}

8. 性能优化和错误处理

8.1 连接池管理

class MCPConnectionPool:
    """MCP连接池"""
    
    def __init__(self, max_connections: int = 10):
        self.max_connections = max_connections
        self.connections: dict[str, asyncio.Queue] = {}
        self.connection_counts: dict[str, int] = {}
    
    async def get_connection(self, server_uuid: str, endpoint: str, auth: dict) -> MCPClient:
        """
        获取MCP连接
        
        Args:
            server_uuid: 服务器UUID
            endpoint: 端点URL
            auth: 认证信息
            
        Returns:
            MCP客户端
        """
        if server_uuid not in self.connections:
            self.connections[server_uuid] = asyncio.Queue(maxsize=self.max_connections)
            self.connection_counts[server_uuid] = 0
        
        try:
            # 尝试从连接池获取连接
            client = self.connections[server_uuid].get_nowait()
            return client
        except asyncio.QueueEmpty:
            # 连接池为空，创建新连接
            if self.connection_counts[server_uuid] < self.max_connections:
                client = MCPClient(endpoint, auth)
                await client.connect()
                self.connection_counts[server_uuid] += 1
                return client
            else:
                # 等待可用连接
                client = await self.connections[server_uuid].get()
                return client
    
    async def release_connection(self, server_uuid: str, client: MCPClient):
        """
        释放MCP连接
        
        Args:
            server_uuid: 服务器UUID
            client: MCP客户端
        """
        if server_uuid in self.connections:
            try:
                self.connections[server_uuid].put_nowait(client)
            except asyncio.QueueFull:
                # 连接池已满，关闭连接
                await client.close()
                self.connection_counts[server_uuid] -= 1

8.2 错误处理和重试机制

class MCPErrorHandler:
    """MCP错误处理器"""
    
    def __init__(self, max_retries: int = 3):
        self.max_retries = max_retries
    
    async def execute_with_retry(self, func: Callable, *args, **kwargs):
        """
        带重试机制的执行
        
        Args:
            func: 要执行的函数
            *args: 位置参数
            **kwargs: 关键字参数
            
        Returns:
            执行结果
        """
        for attempt in range(self.max_retries + 1):
            try:
                return await func(*args, **kwargs)
            except (aiohttp.ClientError, asyncio.TimeoutError) as e:
                if attempt == self.max_retries:
                    raise e
                # 指数退避
                await asyncio.sleep(2 ** attempt)
            except Exception as e:
                # 其他错误不重试
                raise e

总结

LangBot对MCP协议的支持为聊天机器人应用提供了强大的扩展能力。通过标准化的协议接口，开发者可以轻松集成各种外部工具和服务，极大地丰富了机器人的功能。

关键要点包括：

1. 协议兼容：完全兼容MCP协议标准，支持工具调用、资源访问等功能
2. 灵活集成：支持多个MCP服务器同时连接和管理
3. 工具映射：将MCP工具无缝映射为LangBot内部工具
4. 性能优化：通过连接池、重试机制等技术优化性能
5. 扩展支持：支持自定义方法扩展和插件集成

在实际应用中，建议遵循以下最佳实践：

1. 合理配置：根据实际需求配置MCP服务器连接参数
2. 错误处理：完善错误处理机制，确保系统稳定性
3. 性能优化：使用连接池等技术优化连接性能
4. 安全认证：正确配置认证信息，确保通信安全
5. 监控日志：记录详细的日志信息，便于问题排查

通过合理使用LangBot的MCP协议支持，开发者可以构建出功能丰富、扩展性强的智能聊天机器人应用，充分利用外部工具和服务的能力。

参考资料

1. MCP协议官方文档
2. LangBot官方文档 - MCP支持
3. MCP模块源码
4. 协议适配器实现