How to Use the New Relic AI (LLM Observability) MCP in Google ADK

Q: How do I add New Relic AI (LLM Observability) to Google ADK?

Install the server via the McpToolset class. You pass the server parameters to your LlmAgent initialization to expose the tools.

Q: Will New Relic AI (LLM Observability) work with Google ADK custom tools?

Yes. You can use the toolnames filter in your McpToolset to restrict which telemetry tools your agent can access.

Bring enterprise telemetry into Google ADK agents for better control over your Gemini-based infrastructure.

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Connect New Relic AI (LLM Observability) MCP to Google ADK

Create your Vinkius account to connect New Relic AI (LLM Observability) to Google ADK 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 AI (LLM Observability) with Google ADK

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Monitor infrastructure with Google ADK

Connect your agents to your existing cloud metrics. Use `list_apm_apps` to identify which parts of your stack are generating the most telemetry. Your agent can now list and filter active applications. This ensures your Gemini-powered agents are always aware of the broader system state.

Manage alerts in Google ADK

Keep your monitoring in sync with your agent's actions. Use `list_alert_policies` to check which thresholds are currently active in your environment. Your agent can now verify if an alert policy exists before it makes a critical decision. This prevents your agent from acting blindly during a system incident.

Analyze feedback loops in Google ADK

Improve agent performance using actual usage data. Use `query_llm_feedback` to see how your users react to generated content. This tool allows your agent to refine its output based on real results. You get a direct loop between user sentiment and agent behavior.

Setup guide

Set up New Relic AI (LLM Observability) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with New Relic AI (LLM Observability) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="New Relic AI (LLM Observability)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to New Relic AI (LLM Observability) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about New Relic AI (LLM Observability) MCP in Google ADK

Install the server via the `McpToolset` class. You pass the server parameters to your `LlmAgent` initialization to expose the tools.

Yes. You can use the tool_names filter in your `McpToolset` to restrict which telemetry tools your agent can access.

The server supports HTTP transports, which fit perfectly with your agent's async flow. It allows for non-blocking telemetry checks while the agent processes data.

Vinkius runs this in a sandboxed environment. Your telemetry data is transmitted over encrypted channels without exposing your underlying cloud keys.

The server only reads telemetry logs like latency and error counts. It does not store your content or training data in any persistent form.

Use it with your favorite AI tools

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