How to Use the New Relic MCP in LlamaIndex

Index your entire New Relic infrastructure into LlamaIndex for searchable, grounded observability data.

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Connect New Relic MCP to LlamaIndex

Create your Vinkius account to connect New Relic to LlamaIndex 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 LlamaIndex

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Index New Relic data with LlamaIndex

Convert `list_dashboards` and `get_dashboard` configurations into vector embeddings. Your knowledge base now contains the exact state of your monitoring setup. This allows your agent to answer questions about dashboard layout without querying the live API every time. It keeps your RAG application fast and responsive.

Ground LlamaIndex answers in real telemetry

Fetch live data using `run_nrql` and feed it into your index. Your model stops hallucinating about system health because it references current query results. This creates a unified source of truth for your technical documentation and live performance metrics. You get accurate, data-backed insights for every query.

Filter New Relic tools in LlamaIndex

Use the allowed_tools filter to limit which parts of your monitoring stack your agent can access. You control exactly what data enters your vector store. This restriction prevents your agent from running expensive queries on non-essential accounts. It focuses the model on relevant telemetry during the indexing phase.

Setup guide

Set up New Relic MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all New Relic MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to New Relic tools.",
)
response = await agent.run("List recent New Relic data")

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Why Choose Vinkius

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Common questions about New Relic MCP in LlamaIndex

Install the MCP tools package and use the BasicMCPClient to point at your server. Convert the tool spec into an async list and pass it to your FunctionAgent.

The framework indexes the output, allowing you to search through your configuration history. This makes it simple to find specific alert thresholds or service definitions.

Yes, once you add the alert listing tool to your agent, the output is indexed just like any other document. You can then ask questions about active issues directly.

Vinkius provides a secure endpoint for your server, ensuring all API calls are authenticated via a single token. Your account credentials stay completely shielded from the indexing process.

Your retrieved alert logs are processed within your private vector store. We ensure that this sensitive operational data remains restricted to your authorized index only.

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