How to Use the Airbrake MCP in LangChain

Q: How can a LangChain agent check Airbrake for new errors after a deploy?

Your agent uses the trackdeploy tool to mark the deployment time. Then, it can periodically call listerrorgroups and filter for errors that appeared after that marker.

Q: What's the best way to report new issues to Airbrake from a LangChain agent?

Use the reportnotice tool. Your agent can construct the error payload itself, maybe even enriching it with context from previous steps in the chain, and send it directly to the correct project.

Q: How does my agent know which projects to monitor?

It can start by calling the listprojects tool. This gives it a complete list of available projects, which it can then iterate through to perform checks or look for errors.

Build agents that automatically investigate and report Airbrake errors with LangChain.

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Connect Airbrake MCP to LangChain

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Setup Airbrake with LangChain

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Chain Tools for Root Cause Analysis

Give your agent a goal, not a script. It can start by calling `list_error_groups` to find active issues, pick the most critical one, and then automatically call `get_error_group` to get the details. It doesn't stop there. Next, the agent can use `list_notices` for that specific group to dig into individual error instances. By chaining these tools together, your LangChain agent pieces together the full story of a bug, from initial alert to specific stack trace, all on its own.

Connect Deployments to New Errors

Don't just monitor errors, correlate them. Your agent can call `track_deploy` the moment your CI/CD pipeline finishes. From that point on, it can keep an eye on things. Then, set up a chain that periodically runs `list_error_groups` and looks for anything new since the deploy marker. If it finds a new error, it has the context. It knows the deploy is the likely cause and can report the issue with all the relevant data included.

Automate Audits with a LangChain MCP Server

This MCP Server is perfect for building autonomous health checks. Create a scheduled agent that wakes up once a day and calls `list_projects` to get a full inventory of what it needs to check. From there, it iterates through each project, using `list_error_groups` to look for unresolved issues or spikes in activity. It can compile a single report and send it to you, so you start your day knowing exactly where the problems are.

Setup guide

Set up Airbrake 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 Airbrake 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({
    "airbrake-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 Airbrake transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Airbrake MCP in LangChain

Your agent uses the `track_deploy` tool to mark the deployment time. Then, it can periodically call `list_error_groups` and filter for errors that appeared after that marker.

Yes, that's exactly how it works. One part of the chain calls `list_error_groups` to find an ID, and the next part passes that ID to the `get_error_group` tool to fetch the details.

Use the `report_notice` tool. Your agent can construct the error payload itself, maybe even enriching it with context from previous steps in the chain, and send it directly to the correct project.

It can start by calling the `list_projects` tool. This gives it a complete list of available projects, which it can then iterate through to perform checks or look for errors.

Yes, data like error group details and notice contents are proxied by Vinkius. Each request runs in an ephemeral, zero-trust sandbox, and your Vinkius endpoint token handles all the authentication with Airbrake for your MCP tool calls.

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