MCP进阶：工业协议与AI智能体的融合革命

# 上下文动态更新示例
from mcp.context import MCPContext

ctx = MCPContext(working_size=8192, persistent_size=65536)
ctx.update(new_tokens, 
           salient_threshold=0.7,  # 显著值阈值
           sharing_policy="semantic"  # 语义相似度广播
)

import torch.nn as nn

class MCPAttention(nn.Module):
    def __init__(self, d_model, n_heads):
        super().__init__()
        # 三种上下文的投影矩阵
        self.k_projs = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
        self.v_projs = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])

        # 动态门控网络
        self.gating_net = nn.Sequential(
            nn.Linear(d_model, 32), 
            nn.ReLU(),
            nn.Linear(32, 3),
            nn.Softmax(dim=-1)
        )

    def forward(self, x, contexts):
        # 计算各上下文注意力
        attn_outputs = []
        for i, ctx in enumerate(contexts):
            k = self.k_projs[i](ctx)
            v = self.v_projs[i](ctx)
            attn = torch.matmul(x, k.transpose(-2, -1)) 
            attn_outputs.append(torch.matmul(attn, v))

        # 动态权重分配 [4](@ref)
        gate_weights = self.gating_net(x) 
        return sum(w * out for w, out in zip(gate_weights, attn_outputs))

# MCP服务：光伏逆变器健康诊断
@mcp.tool(description="分析逆变器状态")
def analyze_inverter(ctx_id: str) -> dict:
    # 从持久上下文获取历史数据
    hist_data = mcp_server.query_context(ctx_id, path="/inverter/history")

    # 调用工业MCP网关读取实时寄存器
    reg_values = modbus_client.read_registers(slave_id=1, address=30001, count=10)

    # 使用AI模型预测故障概率
    model = load_model("inverter_fault_detector.pkl")
    risk_score = model.predict([hist_data + reg_values])

    # 更新共享上下文
    mcp_server.update_context(ctx_id, 
                              path="/alerts", 
                              value={"risk_level": risk_score},
                              mode="MERGE"
                             )
    return {"status": "success", "risk_score": risk_score}

{
  "name": "kinetic_energy",
  "description": "计算运动物体的动能",
  "parameters": [
    {"name": "mass", "type": "float", "constraints": ">0"},
    {"name": "velocity", "type": "float"}
  ],
  "return_type": "float"
}

// Go实现的安全隔离沙箱
type ContextSandbox struct {
    baseModel   *llm.Model
    safetyCheck SafetyChecker
}

func (s *ContextSandbox) Execute(ctx Context) (Response, error) {
    // 1. 内容安全检查
    if score := s.safetyCheck.Score(ctx); score < 0.5 {
        return nil, ErrUnsafeContext
    }

    // 2. 敏感数据脱敏
    sanitizedCtx := sanitizer.RemovePII(ctx) 

    // 3. 资源限制执行
    result := s.baseModel.Run(sanitizedCtx, 
                              MaxCPUTime(100*time.Millisecond),
                              MaxMemory(512 * 1024 * 1024)
                             )

    // 4. 输出过滤
    return filter.SensitiveWords(result), nil
}