Add MCP support for clearing read notifications

bots/reply_bots.ts

@@ -3,19 +3,24 @@ import { Agent, Runner, hostedMcpTool, withTrace } from "@openai/agents";
 
 console.log("✅ Reply bot starting...");
 
+const allowedMcpTools = [
+  "search",
+  "reply_to_post",
+  "reply_to_comment",
+  "recent_posts",
+  "get_post",
+  "list_unread_messages",
+  "mark_notifications_read",
+];
+
+console.log("🛠️ Configured Hosted MCP tools:", allowedMcpTools.join(", "));
+
 // ---- MCP 工具（Hosted MCP） ----
 // 关键点：requireApproval 设为 "never"，避免卡在人工批准。
 const mcp = hostedMcpTool({
   serverLabel: "openisle_mcp",
   serverUrl: "https://www.open-isle.com/mcp",
-  allowedTools: [
-    "search",
-    "reply_to_post",
-    "reply_to_comment",
-    "recent_posts",
-    "get_post",
-    "list_unread_messages"
-  ],
+  allowedTools: allowedMcpTools,
   requireApproval: "never",
 });
 
@@ -24,6 +29,12 @@ type WorkflowInput = { input_as_text: string };
 // 从环境变量读取你的站点鉴权令牌（可选）
 const OPENISLE_TOKEN = process.env.OPENISLE_TOKEN ?? "";
 
+console.log(
+  OPENISLE_TOKEN
+    ? "🔑 OPENISLE_TOKEN detected in environment."
+    : "🔓 OPENISLE_TOKEN not set; agent will request it if required."
+);
+
 // ---- 定义 Agent ----
 const openisleBot = new Agent({
   name: "OpenIsle Bot",
@@ -34,6 +45,7 @@ const openisleBot = new Agent({
     OPENISLE_TOKEN
       ? `If tools require auth, use this token exactly where the tool schema expects it: ${OPENISLE_TOKEN}`
       : "If a tool requires auth, ask me to provide OPENISLE_TOKEN via env.",
+    "After finishing replies, call mark_notifications_read with all processed notification IDs to keep the inbox clean.",
   ].join("\n"),
   tools: [mcp],
   model: "gpt-4o",
@@ -63,12 +75,21 @@ export const runWorkflow = async (workflow: WorkflowInput) => {
   });
 
   return await withTrace("OpenIsle Bot run", async () => {
+    const preview = workflow.input_as_text.trim();
+    console.log(
+      "📝 Received workflow input (preview):",
+      preview.length > 200 ? `${preview.slice(0, 200)}…` : preview
+    );
+
     // Runner.run 会自动循环执行：LLM → 工具 → 直至 finalOutput
+    console.log("🚦 Starting agent run with maxTurns=16...");
     const result = await runner.run(openisleBot, workflow.input_as_text, {
       maxTurns: 16, // 允许更复杂任务多轮调用 MCP
       // stream: true  // 如需边跑边看事件可打开，然后消费流事件
     });
 
+    console.log("📬 Agent run completed. Result keys:", Object.keys(result));
+
     if (!result.finalOutput) {
       // 若没产出最终结果，通常是启用了人工批准/工具失败/达到 maxTurns
       throw new Error("Agent result is undefined (no final output).");
@@ -76,7 +97,11 @@ export const runWorkflow = async (workflow: WorkflowInput) => {
 
     const openisleBotResult = { output_text: String(result.finalOutput) };
 
-    console.log("🤖 Agent result:\n" + openisleBotResult.output_text);
+    console.log(
+      "🤖 Agent result (length=%d):\n%s",
+      openisleBotResult.output_text.length,
+      openisleBotResult.output_text
+    );
     return openisleBotResult;
   });
 };
@@ -91,7 +116,8 @@ if (require.main === module) {
         2）依次处理每条“提及/评论”：如需上下文则使用 get_post 获取，生成简明中文回复；如有 commentId 则用 reply_to_comment，否则用 reply_to_post；
         3）跳过关注和系统事件；
         4）保证幂等性：如该贴最后一条是你自己发的回复，则跳过；
-        5）最多只处理最新10条；结束时仅输出简要摘要（包含URL或ID）。
+        5）调用 mark_notifications_read，传入本次已处理的通知 ID 清理已读；
+        6）最多只处理最新10条；结束时仅输出简要摘要（包含URL或ID）。
       `;
         
       console.log("🔍 Running workflow...");