How to Use the Better Stack MCP in LangChain

Q: Can my LangChain agent acknowledge a Better Stack incident automatically?

Yes. The agent evaluates the alert and calls acknowledgeincident if it matches your predefined criteria. You track the exact decision path in LangSmith.

Build automated incident response pipelines by wiring the Better Stack MCP Server directly into your LangChain agents.

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Connect Better Stack MCP to LangChain

Create your Vinkius account to connect Better Stack 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 Better Stack with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain incident responses with LangChain

LangChain excels at multi-step reasoning. You give it an alert, and it figures out the next move. Connect this MCP integration, and your agent can pull active fires using `list_incidents`. The output feeds straight into the next link in your chain. Once the agent spots a critical outage, it grabs the timeline via `get_incident` and decides whether to halt paging with `acknowledge_incident`.

Tie uptime checks to this MCP Server

Your ReAct agent needs context before waking up an engineer. When a service drops, the agent runs `list_monitors` to find the exact endpoint failing. It then pulls the passive node details using `get_heartbeat`. You get a complete diagnostic trace in LangSmith, showing exactly which tools fired and how long the API took to respond.

Automate status updates and paging

Manual status page updates waste time during an outage. Your custom chain can grab current routing schedules with `list_on_call` to see who is holding the pager right now. After the engineer fixes the issue, the agent executes `resolve_incident` and checks `list_status_pages` to confirm the public dashboard reflects the fix.

Setup guide

Set up Better Stack 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 Better Stack 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({
    "better-stack-1-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 Better Stack 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 Better Stack. 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.

Why Choose Vinkius

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Common questions about Better Stack MCP in LangChain

Install the adapters using pip. Then initialize a MultiServerMCPClient with your HTTP transport endpoint.

Yes. The agent evaluates the alert and calls `acknowledge_incident` if it matches your predefined criteria. You track the exact decision path in LangSmith.

That is the whole point of chains. You pull a timeline with `get_incident` and pass that payload directly into a Jira or Slack tool.

Raw API calls require custom schemas and error handling. This setup exposes ten typed tools instantly, so your agent knows exactly what inputs the server expects.

The V8 Isolate Sandbox executes your requests ephemerally. Your uptime monitors, incident timelines, and on-call schedules pass through zero-trust memory and vanish immediately after the tool call finishes.

Use it with your favorite AI tools

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