How to Use the Guance Cloud / 观测云 MCP in OpenAI Agents SDK

Q: Can OpenAI Agents SDK use Guance Cloud / 观测云 for automated alerting?

Yes. By chaining listmonitors and listevents, your agent identifies anomalies and triggers responses based on your predefined logic. It removes the manual step of checking the console every time a threshold is breached.

Connect Guance Cloud / 观测云 to your OpenAI Agents SDK production pipeline for automated incident response and observability.

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Connect Guance Cloud / 观测云 MCP to OpenAI Agents SDK

Create your Vinkius account to connect Guance Cloud / 观测云 to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Guance Cloud / 观测云 with OpenAI Agents SDK

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Automate incident triage with OpenAI Agents SDK

Your agent fetches critical data using `list_events` to identify spikes before they hit your dashboard. It then parses the signal, filtering out the noise so you only handle genuine production issues. Once the issue is identified, the agent calls `get_event` to pull the specific stack trace or error log. This gives your system the context needed to resolve the incident without human intervention.

Monitor infrastructure health via MCP Server tools

You can keep tabs on your workspace limits by calling `get_billing` directly from your agent logic. It ensures your production environment stays within budget while maintaining constant visibility. Use `list_monitors` to verify that every critical threshold is active. If a monitor is missing or disabled, the agent flags it immediately for your review.

Run complex DQL queries through your agent

Execute precise data retrieval using `query_data` to pull custom metrics from your infrastructure. It handles raw DQL commands, giving you the ability to transform metrics into actionable insights in real time. Pair this with `list_dashboards` to ensure your agent is referencing the correct source of truth. Your agent maps its findings directly to existing views, keeping your reporting consistent.

Setup guide

Set up Guance Cloud / 观测云 MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Guance Cloud / 观测云 tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Guance Cloud / 观测云 tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Guance Cloud / 观测云 tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Guance Cloud / 观测云 Agent",
            instructions="You have access to Guance Cloud / 观测云 tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Guance Cloud / 观测云 MCP in OpenAI Agents SDK

Vinkius handles the heavy lifting by managing your endpoint tokens securely. You simply inject the token into your server connection, and the MCP Server handles the rest without exposing raw credentials in your codebase.

Yes. By chaining `list_monitors` and `list_events`, your agent identifies anomalies and triggers responses based on your predefined logic. It removes the manual step of checking the console every time a threshold is breached.

It is built for it. The server supports async operations, allowing your agents to perform multiple checks simultaneously without blocking your main execution thread.

The MCP Server returns a clear error message that your agent interprets immediately. You can catch these exceptions in your Python code to log the failure or attempt a retry.

Your observability data, including log sources and event metadata, stays within your workspace. The connection is ephemeral, meaning no persistent data is stored on the Vinkius infrastructure during your session.

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