Python
# 最小化Client创建
from mcp_sdk import MCPClient

# 基础配置
config = {
    "registry_endpoint": "https://registry.mcp.org",
    "context_service": "https://ctx.mcp.org:443"
}

# 创建客户端实例
client = MCPClient.basic_client(config)

# 执行简单任务
result = client.execute(
    task_description="分析这张图片中的物体",
    input_data={"image": open("photo.jpg", "rb")},
    output_format="text"
)

Java
// Java高级客户端示例
import org.mcp.sdk.*;

public class AdvancedClient {
    public static void main(String[] args) {
        // 构建器模式配置
        MCPClient client = new MCPClient.Builder()
            .withRegistry(new ZookeeperRegistry("zk.mcp.org:2181"))
            .withContextService(new DistributedContextService()
                .setReplicationFactor(3))
            .withSecurity(new MTLSSecurity()
                .setCertPath("/certs/client.pem")
                .setKeyPath("/certs/client.key"))
            .withRoutingPolicy(new SmartRouter()
                .setCostFactors(LATENCY | ACCURACY)
                .setFallbackMode(GRADUAL_DEGRADATION))
            .withMonitoring(new PrometheusMonitor()
                .setFrequency(5, TimeUnit.SECONDS))
            .build();
       
        // 创建异步任务
        TaskFuture future = client.executeAsync(
            new TaskRequest()
                .setContextId("ctx_12345")
                .addInput("audio", Audio.load("record.wav"))
                .setOutputSpec(OutputSpec.TEXT_SUMMARY)
        );
       
        // 非阻塞获取结果
        future.onComplete(result -> {
            System.out.println("分析结果：" + result.getOutput("summary"));
        });
    }
}

配置类别

关键参数

说明

默认值

注册中心

registry_type

类型(zookeeper/etcd/consul)

zookeeper

registry_endpoints

集群节点地址

localhost:2181

上下文服务

context_backend

存储类型(S3/Cassandra/Redis)

Cassandra

context_ttl

上下文存活时间(小时)

72

路由策略

routing_algorithm

路由算法(priority/random/cost)

cost-based

fallback_strategy

故障转移策略

fast-failover

安全认证

auth_mode

认证方式(mTLS/OAuth)

mTLS

cert_refresh_interval

证书刷新间隔(分钟)

1440

性能优化

connection_pool_size

连接池大小

20

max_retries

最大重试次数

3

Go
// Go语言上下文管理器核心逻辑
type ContextManager struct {
    storage ContextStorage
    cache   *LRUCache
}

func (cm *ContextManager) GetContext(ctxID string) (ContextContainer, error) {
    // 优先查询缓存
    if ctx, hit := cm.cache.Get(ctxID); hit {
        return ctx.(ContextContainer), nil
    }
   
    // 缓存未命中时查询存储
    ctx, err := cm.storage.Load(ctxID)
    if err != nil {
        return ContextContainer{}, err
    }
   
    // 更新缓存
    cm.cache.Set(ctxID, ctx)
    return ctx, nil
}

func (cm *ContextManager) UpdateContext(ctx ContextContainer) error {
    // 写入存储
    if err := cm.storage.Save(ctx); err != nil {
        return err
    }
   
    // 更新缓存
    cm.cache.Set(ctx.ID, ctx)
   
    // 触发变更通知
    event := ContextEvent{
        ID:        ctx.ID,
        Operation: UPDATE,
        Timestamp: time.Now(),
    }
    cm.notifier.Publish(event)
    return nil
}

Python
# Python路由决策引擎
class SmartRouter:
    def __init__(self, registry):
        self.registry = registry
        self.load_balancer = LeastConnectionsBalancer()
   
    def select_model(self, capability, context=None):
        # 获取候选模型
        candidates = self.registry.get_models(capability)
       
        # 上下文感知过滤
        if context:
            candidates = self._filter_by_context(candidates, context)
       
        # 健康检查
        healthy_candidates = [m for m in candidates if m.health_status == HEALTHY]
       
        # 负载均衡选择
        selected = self.load_balancer.select(healthy_candidates)
        
        return selected.endpoint

    def _filter_by_context(self, models, context):
        # 根据上下文元数据过滤
        filtered = []
        for model in models:
            # 检查计算资源需求
            if context.require_gpu and not model.supports_gpu:
                continue
               
            # 检查数据格式兼容性
            if not self._check_compatibility(model.input_schema, context.data_formats):
                continue
               
            filtered.append(model)
        return filtered

Python
# 提前加载可能用到的上下文
client.prefetch_context("ctx_12345")

Java
// 合并多个模型请求
BatchRequest batch = new BatchRequest();
batch.addTask(task1);
batch.addTask(task2);
BatchResponse response = client.executeBatch(batch);

JavaScript
// 仅同步修改的上下文部分
const changes = {
  "detection_results": newResults,
  "timestamp": Date.now()
};
client.updateContext("ctx_123", changes);

YAML
# 多级缓存配置示例
context_cache:
  level1:  # 内存缓存
    size: 1000
    ttl: 5m
  level2:  # 本地磁盘缓存
    path: /var/mcp/cache
    size: 100GB
    ttl: 24h

Python
# 图像分析流水线
pipeline = client.build_pipeline(
    steps=[
        {"model": "object-detection", "input": "$.image"},
        {"model": "ocr", "input": "$.detection.regions"},
        {"model": "text-translation", "input": "$.ocr.text"},
        {"model": "report-generator", "input": "$.translation.result"}
    ]
)
result = pipeline.execute(image=product_image)

JavaScript
// 智能客服场景
const response = await client.dynamicRoute({
  query: "这张发票能报销吗？",
  attachments: [invoiceImage],
  context: userSessionContext,
  decisionTree: {
    if: "$contains(image)",
    then: ["invoice-parser", "policy-checker"],
    else: ["intent-classifier", "qa-system"]
  }
});

Java
// 持续对话场景
ContextContainer ctx = client.getContext("conv_123");
ctx.update("user_preference", new JSONObject("{'language':'zh'}"));

TextResponse resp = client.execute(
  new ModelRequest("dialog-system")
    .setInput("user_query", "帮我订机票")
    .setContext(ctx)
);

ctx.merge(resp.getContextUpdates());
client.saveContext(ctx);