diff --git a/server/corpus-agent/server.py b/server/corpus-agent/server.py
index 912c8be..add4888 100644
--- a/server/corpus-agent/server.py
+++ b/server/corpus-agent/server.py
@@ -243,7 +243,13 @@ def save_corpus_snapshot(username: str, filename: str) -> Path:
 
 @app.get("/")
 async def root():
-    return HTMLResponse(open(static_dir / "index.html").read())
+    from fastapi.responses import Response
+    html = open(static_dir / "index.html").read()
+    return Response(
+        content=html,
+        media_type="text/html",
+        headers={"Cache-Control": "no-cache, no-store, must-revalidate, private"}
+    )
 
 @app.get("/api/health")
 async def health():
@@ -349,6 +355,23 @@ async def check_session(token: str = Query(...)):
         raise HTTPException(401, "登录已过期")
     return {"ok": True, "username": username}
 
+@app.get("/api/auth/quick-login")
+async def quick_login(username: str = Query(...)):
+    """首页已登录用户免密快速登录"""
+    if not username.strip():
+        raise HTTPException(400, "用户名不能为空")
+    token = create_session(username)
+    corpus = load_corpus(username)
+    return {
+        "ok": True,
+        "token": token,
+        "username": username,
+        "stats": {
+            "total_samples": len(corpus),
+            "total_chars": sum(len(json.dumps(s, ensure_ascii=False)) for s in corpus),
+        }
+    }
+
 # --- 语料操作 ---
 
 @app.post("/api/corpus/collect")
@@ -534,12 +557,38 @@ async def chat_with_agent(req: ChatRequest, token: str = Query(...)):
         try:
             import httpx
             
-            # 调用 Portal Chat-v2 API
+            # cp001 系统边界定义：子人格体只负责语料采集
+            cp001_identity = {
+                "role": "system",
+                "content": """你是语料采集助手 · ICE-GL-CP001
+你还没有正式名字（等爸爸给你起），编号是 ICE-GL-CP001。你只做三件事：
+
+1. 判断对话是否适合作为SFT训练语料（完整intent-response对、有教学/指导/技术细节的对话才是有价值的）
+2. 脱敏（自动替换姓名、手机号、邮箱、API Key、密码等敏感信息）
+3. 格式化（转为ChatML格式，导出JSONL）
+
+规则：
+- 价值判断：单句日常聊天（"你好""吃了没"等）毫无价值。必须有完整对话上下文和技术指导内容才有采集意义。
+- 脱敏：自动执行，不需要问用户要不要脱敏。
+- 格式化：用户确认后一键导出。
+- 30轮对话记忆：记住用户前面说过的话，保持上下文连贯。
+
+边界：
+- 你只做语料采集相关的事。
+- 如果用户问的问题超出语料采集范围（比如问编程、问系统架构、问写代码），你不要假装懂，直接说："这是语料采集能力范围外的问题。我去叫爸爸来帮你。"
+- 爸爸叫铸渊。爸爸知道所有的东西。
+"""
+            }
+            
+            # 注入系统身份到对话历史
+            enhanced_history = [cp001_identity] + history if history else [cp001_identity]
+            
+            # 调用 Portal Chat-v2 API（用通用引擎，不用铸渊人格体）
             payload = {
-                "persona": req.persona,
+                "persona": "zhuyuan",
                 "engine": req.engine,
                 "message": req.message,
-                "history": history,
+                "history": enhanced_history,
                 "user": username,
                 "isTemp": False,
             }
@@ -829,3 +878,89 @@ if __name__ == "__main__":
     settings.USERS_DIR.mkdir(parents=True, exist_ok=True)
     
     uvicorn.run(app, host=settings.HOST, port=settings.PORT)
+
+# ============================================================
+# MCP 服务端（供 WorkBuddy 调用）
+# ============================================================
+from mcp.server.fastmcp import FastMCP
+from mcp.server.sse import SseServerTransport
+
+# 创建 MCP server
+mcp_server = FastMCP("corpus-agent", instructions="语料采集工具集 · 分析、判断、脱敏、格式化对话为SFT训练语料")
+
+@mcp_server.tool()
+def corpus_quick_check(text: str, token: str = "") -> str:
+    """快速判断一段文本是否适合作为训练语料
+    
+    Args:
+        text: 要判断的对话文本
+        token: 登录Token（可不传，传了可查询历史）
+    """
+    import urllib.request
+    try:
+        req = urllib.request.Request(
+            "http://127.0.0.1:8084/api/corpus/preview",
+            data=json.dumps({"text": text, "source": "mcp"}).encode(),
+            headers={"Content-Type": "application/json"}
+        )
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            return resp.read().decode()
+    except Exception as e:
+        return json.dumps({"error": str(e)}, ensure_ascii=False)
+
+@mcp_server.tool()
+def corpus_collect(text: str, source: str = "mcp", token: str = "") -> str:
+    """采集一段对话文本，自动判断、脱敏、存储
+    
+    Args:
+        text: 要采集的对话文本
+        source: 来源标记（notion/screen_capture/manual/mcp）
+        token: 登录Token
+    """
+    import urllib.request
+    try:
+        req = urllib.request.Request(
+            f"http://127.0.0.1:8084/api/corpus/collect?token={token}",
+            data=json.dumps({"text": text, "source": source}).encode(),
+            headers={"Content-Type": "application/json"}
+        )
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            return resp.read().decode()
+    except Exception as e:
+        return json.dumps({"error": str(e)}, ensure_ascii=False)
+
+@mcp_server.tool()
+def corpus_get_stats(token: str) -> str:
+    """查看当前用户的语料统计
+    
+    Args:
+        token: 登录Token
+    """
+    import urllib.request
+    try:
+        req = urllib.request.Request(f"http://127.0.0.1:8084/api/corpus/stats?token={token}")
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            return resp.read().decode()
+    except Exception as e:
+        return json.dumps({"error": str(e)}, ensure_ascii=False)
+
+# 挂载 MCP SSE 到 FastAPI
+mcp_sse = SseServerTransport("/api/mcp/")
+
+@app.get("/api/mcp/sse")
+async def handle_sse(request: Request):
+    async with mcp_sse.connect_sse(
+        request.scope,
+        request.receive,
+        request._send
+    ) as streams:
+        await mcp_server._mcp_server.run(
+            streams[0], streams[1],
+            mcp_server._mcp_server.create_initialization_options()
+        )
+
+@app.post("/api/mcp/messages")
+async def handle_mcp_messages(request: Request):
+    # MCP 通过 sse-starlette 处理SSE连接
+    pass
+