客户端最佳实践

// 模型调用一个轻量级搜索工具
search_tools({ query: "update salesforce record" })

// 返回简明匹配结果：仅包含名称和一行描述
→ [
    { name: "salesforce_updateRecord", description: "Update fields on a Salesforce object" },
    { name: "salesforce_upsertRecord", description: "Insert or update based on external ID" }
  ]

// 模型只检查它需要的工具
get_tool_details({ name: "salesforce_updateRecord" });

{
  "name": "salesforce_updateRecord",
  "description": "Updates a record in Salesforce",
  "inputSchema": {
    "type": "object",
    "properties": {
      "objectType": {
        "type": "string",
        "description": "Salesforce 对象类型"
      },
      "recordId": { "type": "string", "description": "要更新的记录 ID" },
      "data": { "type": "object", "description": "要更新的字段" }
    },
    "required": ["objectType", "recordId", "data"]
  }
}

// 根据 Logging MCP 服务器的工具模式自动生成
interface LogEntry {
  timestamp: string;
  message: string;
  level: string;
}

function logging_getLogs(input: {
  level: "error" | "warn" | "info";
  since: number;
}): Promise<{ entries: LogEntry[] }> {
  return mcp.callTool<{ entries: LogEntry[] }>("logging_getLogs", input);
}

// 根据 Ticketing MCP 服务器的工具模式自动生成
function ticketing_createIssue(input: {
  title: string;
  body?: string;
  priority: "low" | "medium" | "high";
}): Promise<{ issueId: string }> {
  return mcp.callTool<{ issueId: string }>("ticketing_createIssue", input);
}

// 模型生成的代码，在沙箱中执行
const logs = await logging_getLogs({
  level: "error",
  since: Date.now() - 3600000,
});

// 在沙箱内过滤并去重，而不是在模型上下文中处理
const uniqueErrors = new Map<string, LogEntry>();
for (const log of logs.entries) {
  if (!uniqueErrors.has(log.message)) {
    uniqueErrors.set(log.message, log);
  }
}

for (const [message, log] of uniqueErrors) {
  await ticketing_createIssue({
    title: `错误：${message}`,
    body: `首次出现：${log.timestamp}\n出现次数：${
      logs.entries.filter((l) => l.message === message).length
    }`,
    priority: "high",
  });
}

console.log(
  `根据 ${logs.entries.length} 条错误日志创建了 ${uniqueErrors.size} 张工单`,
);