How to Use the Guance Cloud / 观测云 MCP in LangChain

Q: Can a LangChain agent query specific workspace metrics from Guance Cloud / 观测云?

Yes, the agent uses querydata to run DQL queries directly against your workspaces. You just need to pass the workspace context to the agent during execution.

Get your LangChain agents querying metrics and managing monitors directly on Guance Cloud using this MCP Server.

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Connect Guance Cloud / 观测云 MCP to LangChain

Create your Vinkius account to connect Guance Cloud / 观测云 to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run complex multi-step incident triage chains

Your LangChain agent uses `list_events` to spot active issues and immediately kicks off a triage chain. Instead of hardcoding logic, you let the agent decide when to pull deep telemetry with `query_data` based on the event payload. The output of one tool feeds directly into the next step. If an event indicates a database spike, the agent queries the specific workspace using `get_workspace` and checks active monitors via `list_monitors` to pinpoint the root cause before you even look at the console.

Trace MCP Server telemetry queries with LangSmith

This MCP Server exposes Guance Cloud tools as agent-ready functions. You get full visibility into how your agent builds queries. You can track latency and token usage in LangSmith when the agent executes `query_data` or checks `get_monitor`. If an agent gets stuck in a loop trying to find a broken dashboard, you will see the exact sequence of `list_dashboards` calls. It makes debugging agent behaviors as simple as debugging standard application code.

Track infrastructure billing through pipeline runs

The agent uses `get_billing` to correlate your infrastructure costs with current system load. By feeding billing data directly into your LangChain chains, you can build automated cost-reporting agents that flag sudden budget spikes. You do not have to guess where the costs come from. The agent pulls active workspaces with `get_workspace` and correlates them with billing metrics to give you a clear picture of resource consumption across teams.

Setup guide

Set up Guance Cloud / 观测云 MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Guance Cloud / 观测云 tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "guance-cloud-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Guance Cloud / 观测云 transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Guance Cloud / 观测云. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Guance Cloud / 观测云 MCP in LangChain

Install the langchain-mcp-adapters and langgraph packages first. Then use the MultiServerMCPClient to point to the hosted endpoint and register the tools with your agent.

Yes, the agent uses `query_data` to run DQL queries directly against your workspaces. You just need to pass the workspace context to the agent during execution.

The agent catches tool exceptions and can use its reasoning loop to retry. For instance, if `list_events` fails due to an invalid filter, the agent inspects the error and rewrites the query parameters.

LangChain supports multi-server MCP aggregation out of the box. You can pair this telemetry server with a Slack server to post incident updates automatically.

The server handles your workspace access keys in a secure, ephemeral V8 sandbox. Your DQL queries, workspace details, and telemetry logs are processed in-memory and never stored or shared outside the execution context.

Use it with your favorite AI tools

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