How to Use the New Relic MCP in AutoGen

Let your AutoGen agents debate New Relic performance data to reach consensus on incident response.

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Connect New Relic MCP to AutoGen

Create your Vinkius account to connect New Relic to AutoGen 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 New Relic with AutoGen

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Debate New Relic alerts in AutoGen

Set up a security agent and a performance agent to review `list_alerts` output together. They compare findings to decide if an alert is a false positive. This multi-agent approach prevents hasty decisions during an outage. They cross-reference the alert with `get_entity_details` to verify the scope of the impact.

Negotiate New Relic queries in AutoGen

An analyst agent requests a `run_nrql` query while a cost-conscious agent reviews the complexity. They negotiate the query parameters to optimize for speed and coverage. This ensures your team doesn't burn through query limits while troubleshooting. The agents converge on a solution that provides the best signal-to-noise ratio.

Automate New Relic reporting with AutoGen

Assign an agent to monitor `list_service_levels` and report back to the group. If an SLO drops, the agents trigger a discussion on the root cause. This turns your monitoring into an active conversation. You get a summary of the debate and the final consensus on what steps to take next.

Setup guide

Set up New Relic MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes New Relic tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="New Relic_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent New Relic data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="New Relic_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent New Relic data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about New Relic MCP in AutoGen

Use the MCP tool adapter to convert server tools into functions your agents can call. Pass this tool list to your AssistantAgent constructor to begin the conversation.

Yes, the server supports streamable HTTP, allowing your entire agent team to call tools as needed. They coordinate their requests through the shared tool adapter.

Agents challenge each other's interpretations of the telemetry. This debate helps the system reach a more accurate conclusion than a single agent could provide.

Authentication is handled at the server level via your Vinkius endpoint token. Your agents operate within a sandbox that keeps your account access private.

The entity metadata accessed by your agents is kept in an ephemeral memory space. No other party can access or view the specific infrastructure details your agents analyze.

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