feat(agent): Jina 搜索改用标准 MCP SDK 接入

- agent/task_executor.py: Jina 搜索从手搓 httpx 改为官方 mcp SDK
  (streamablehttp_client + ClientSession);工具经 OpenAI function-calling 暴露给模型
- agent/requirements.txt: +mcp==1.28.0;pydantic 2.9.2->2.13.4(mcp 要求 >=2.11)
- orchestrator/agent_launcher.py: JINA_API_KEY 经 per-swarm Secret 透传给 agent pod
  (SENSITIVE_ENV_KEYS),不内联 PodSpec
- k8s/orchestrator-local.yaml: 本地部署清单(默认 in-pod 沙箱评估开关 + JINA_API_KEY)

沙箱保持默认 in-pod 方案,未引入 OpenSandbox。
影响范围: agent_swarm(agent/orchestrator) + Agent(新增 Jina MCP 工具)。
密钥经 k8s Secret 注入无明文。不影响 Manager 契约/计费/审计/发布链路。

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
(cherry picked from commit deb984ac38)

agent/requirements.txt

@@ -1,5 +1,8 @@
 openai==1.55.3
 websockets==13.1
-pydantic==2.9.2
+pydantic==2.13.4
 python-dotenv==1.0.1
 prometheus-client==0.20.0
+# Standard MCP SDK — agent connects to Jina MCP (search/read_url) over StreamableHTTP and
+# exposes the tools to the model via OpenAI function-calling. Pulls httpx transitively.
+mcp==1.28.0

agent/task_executor.py

@@ -54,6 +54,11 @@ class TaskExecutor:
         self.usage = self._empty_usage()
         self.current_context: dict = {}
 
+        # Jina MCP — loaded once at startup; empty list means no tools available
+        self.jina_api_key = os.getenv("JINA_API_KEY", "")
+        self._jina_tools: list[dict] = []  # OpenAI-format tool schemas
+        self._jina_tools_loaded = False
+
     async def execute_task(
         self,
         task_id: str,
@@ -466,12 +471,107 @@ Return ONLY the JSON, no other text."""
                 "summary": f"Failed to execute: {e}",
             }
 
+    # Jina MCP endpoint (StreamableHTTP). Read-only web tools (search_web/read_url/…).
+    _JINA_MCP_URL = "https://mcp.jina.ai/v1"
+
+    def _jina_mcp_headers(self) -> dict:
+        return {"Authorization": f"Bearer {self.jina_api_key}"}
+
+    async def _load_jina_tools(self) -> list[dict]:
+        """Fetch tool schemas from Jina MCP via the standard `mcp` SDK (StreamableHTTP transport).
+        Cached after first call. Returns OpenAI function-calling tool specs. The SDK handles the
+        MCP handshake, SSE framing, and session — no hand-rolled JSON-RPC/SSE parsing."""
+        if self._jina_tools_loaded:
+            return self._jina_tools
+        self._jina_tools_loaded = True
+        if not self.jina_api_key:
+            return []
+        try:
+            from mcp import ClientSession
+            from mcp.client.streamable_http import streamablehttp_client
+
+            async with streamablehttp_client(
+                self._JINA_MCP_URL, headers=self._jina_mcp_headers()
+            ) as (read, write, _):
+                async with ClientSession(read, write) as session:
+                    await session.initialize()
+                    tools = (await session.list_tools()).tools
+                    self._jina_tools = [
+                        {
+                            "type": "function",
+                            "function": {
+                                "name": t.name,
+                                "description": t.description or "",
+                                "parameters": t.inputSchema or {"type": "object", "properties": {}},
+                            },
+                        }
+                        for t in tools
+                    ]
+            logger.info("Loaded %d Jina MCP tools (mcp SDK)", len(self._jina_tools))
+        except Exception as exc:
+            logger.warning("Failed to load Jina MCP tools: %s", exc)
+            self._jina_tools = []
+        return self._jina_tools
+
+    async def _call_jina_tool(self, tool_name: str, arguments: dict) -> str:
+        """Invoke a single Jina MCP tool via the standard `mcp` SDK and return its text result."""
+        try:
+            from mcp import ClientSession
+            from mcp.client.streamable_http import streamablehttp_client
+
+            async with streamablehttp_client(
+                self._JINA_MCP_URL, headers=self._jina_mcp_headers()
+            ) as (read, write, _):
+                async with ClientSession(read, write) as session:
+                    await session.initialize()
+                    result = await session.call_tool(tool_name, arguments)
+                    parts = [
+                        c.text for c in result.content
+                        if getattr(c, "type", None) == "text"
+                    ]
+                    return "\n".join(parts) or str(result.content)
+        except Exception as exc:
+            return f"[tool error: {exc}]"
+
     async def _complete(self, prompt: str, max_tokens: int) -> str:
+        """Call the LLM with optional Jina MCP tools; handles the tool-call loop."""
         extra_headers = self._model_attribution_headers()
+        tools = await self._load_jina_tools()
+        messages = [{"role": "user", "content": prompt}]
+
+        for _ in range(8):  # max 8 tool-call rounds
+            kwargs: dict = dict(
+                model=self.model,
+                messages=messages,
+                max_tokens=max_tokens,
+                extra_headers=extra_headers or None,
+            )
+            if tools:
+                kwargs["tools"] = tools
+                kwargs["tool_choice"] = "auto"
+            response = await self.client.chat.completions.create(**kwargs)
+            self._record_openai_usage(response)
+            msg = response.choices[0].message
+
+            if not msg.tool_calls:
+                return msg.content or ""
+
+            # Execute each tool call and feed results back
+            messages.append(msg.model_dump(exclude_unset=True))
+            for tc in msg.tool_calls:
+                args = json.loads(tc.function.arguments or "{}")
+                result = await self._call_jina_tool(tc.function.name, args)
+                logger.info("Jina tool %s → %d chars", tc.function.name, len(result))
+                messages.append({
+                    "role": "tool",
+                    "tool_call_id": tc.id,
+                    "content": result,
+                })
+
+        # Fallback: ask for a final answer without tools
+        messages.append({"role": "user", "content": "Please provide your final answer now."})
         response = await self.client.chat.completions.create(
-            model=self.model,
-            messages=[{"role": "user", "content": prompt}],
-            max_tokens=max_tokens,
+            model=self.model, messages=messages, max_tokens=max_tokens,
             extra_headers=extra_headers or None,
         )
         self._record_openai_usage(response)