How to Use the Dynatrace (APM and Observability) MCP in LangChain

Q: Can a LangChain agent close active alerts in Dynatrace (APM and Observability)?

Yes, the agent can use the closeproblem tool to resolve issues programmatically. This is ideal for auto-remediation chains that verify system recovery before closing out the alert.

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Connect Dynatrace (APM and Observability) MCP to LangChain

Create your Vinkius account to connect Dynatrace (APM and Observability) 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 Dynatrace (APM and Observability) with LangChain

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Multi-step incident triage with LangChain

The `list_problems` tool pulls open alerts directly into your agent's reasoning loop. Your chain analyzes the alert payload, then immediately triggers `get_problem` to extract root-cause details without manual intervention. By linking these steps, your agent decides whether to query performance metrics with `query_metrics` or check recent deployment changes. LangChain traces every single tool execution, giving you a clear audit trail of how the agent reached its conclusion.

Dynamic synthetic monitor management

The `trigger_synthetic_batch` tool executes your browser and HTTP monitors on demand during deployment pipelines. Your agent triggers these tests, waits for execution, and immediately pulls results using `list_synthetic_executions` to verify staging health. If a test fails, the agent uses `update_synthetic_monitor` to adjust configurations or locations. This turns static testing into an active, self-correcting feedback loop within your CI/CD chains.

Direct telemetry injection via MCP Server tools

The `ingest_metrics` tool pushes custom application telemetry directly into your observability platform using the standard line protocol. Your agent takes raw output from databases or external APIs and formats it for instant ingestion. You can also use `ingest_events` to mark deployment milestones or configuration changes. This ensures your performance dashboards stay aligned with actual system events without writing custom integration scripts.

Setup guide

Set up Dynatrace (APM and Observability) 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 Dynatrace (APM and Observability) 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({
    "dynatrace-apm-and-observability-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 Dynatrace (APM and Observability) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Dynatrace (APM and Observability) MCP in LangChain

You don't need to pass raw credentials to your code. Vinkius handles the authentication layer securely, providing a single endpoint token that you pass to the LangChain MCP client setup.

Yes, the agent can use the `close_problem` tool to resolve issues programmatically. This is ideal for auto-remediation chains that verify system recovery before closing out the alert.

The framework manages tool execution sequentially, and the underlying server respects API throttling. If you run dense queries with `query_metrics`, your agent handles the rate-limiting headers gracefully.

Yes. You can register the tools with a LangGraph state machine, letting different agents focus on tasks like synthetic testing or entity mapping.

This MCP server accesses performance metrics, synthetic run logs, and system event data. All communication runs through an ephemeral, zero-trust V8 sandbox on Vinkius, meaning your telemetry data never persists on the hosting platform.

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