New Relic AI (LLM Observability) MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add New Relic AI (LLM Observability) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to New Relic AI (LLM Observability). "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in New Relic AI (LLM Observability)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About New Relic AI (LLM Observability) MCP Server
Connect your New Relic AI account to any AI agent and take full control of your LLM observability, token cost tracking, and performance analytics through natural conversation.
LlamaIndex agents combine New Relic AI (LLM Observability) tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- LLM Telemetry Audit — Retrieve detailed LLM chat completion messages and prompt inputs directly from your agent to understand literal model behavior in real-time
- Token Cost Tracking — Execute structural extraction of model costs to calculate exact USD token consumption across your entire AI infrastructure securely
- Performance Monitoring — Extract p95 latency matrices and average response times to ensure your LLM text generation remains performant and sub-second
- User Feedback Loop — Retrieve chronological feedback messages and 1-5 rating scores dumped by human supervisors to identify quality regressions natively
- Custom NRQL Execution — Run sophisticated read-only queries using the New Relic Query Language (NRQL) to extract rich insights from multi-tenant AI datasets instantly
- Custom Event Injection — Post atomic generic telemetry rows to track internal agent states and custom behavioral markers across your observability pipeline
- Resource Discovery — Enumerate active APM apps, dashboards, and alert policies to audit your AI environment's structural health and PagerDuty configurations
The New Relic AI (LLM Observability) MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect New Relic AI (LLM Observability) to LlamaIndex via MCP
Follow these steps to integrate the New Relic AI (LLM Observability) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from New Relic AI (LLM Observability)
Why Use LlamaIndex with the New Relic AI (LLM Observability) MCP Server
LlamaIndex provides unique advantages when paired with New Relic AI (LLM Observability) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine New Relic AI (LLM Observability) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain New Relic AI (LLM Observability) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query New Relic AI (LLM Observability), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what New Relic AI (LLM Observability) tools were called, what data was returned, and how it influenced the final answer
New Relic AI (LLM Observability) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the New Relic AI (LLM Observability) MCP Server delivers measurable value.
Hybrid search: combine New Relic AI (LLM Observability) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query New Relic AI (LLM Observability) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying New Relic AI (LLM Observability) for fresh data
Analytical workflows: chain New Relic AI (LLM Observability) queries with LlamaIndex's data connectors to build multi-source analytical reports
New Relic AI (LLM Observability) MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect New Relic AI (LLM Observability) to LlamaIndex via MCP:
custom_nrql
Note that NRQL is read-only. Irreversibly vaporize explicit validations extracting rich Churn flags
list_alert_policies
Inspect deep internal arrays mitigating specific Plan Math
list_apm_apps
Dispatch an automated validation check routing explicit Gateway history
list_dashboards
Identify precise active arrays spanning native Gateway auth
post_custom_event
/events` inserting absolute generic `CustomAITelemetry` rows tracking internal agent state. Enumerate explicitly attached structured rules exporting active Billing
query_llm_costs
Perform structural extraction of properties driving active Account logic
query_llm_errors
Identify precise active arrays spanning native Hold parsing
query_llm_events
Identify bounded CRM records inside the Headless New Relic Platform
query_llm_feedback
Retrieve explicit Cloud logging tracing explicit Vault limits
query_llm_latency
Provision a highly-available JSON Payload generating hard Customer bindings
Example Prompts for New Relic AI (LLM Observability) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with New Relic AI (LLM Observability) immediately.
"Show me the last 5 LLM events for the 'OpenAI' vendor"
"What is my total LLM token cost for the last 24 hours?"
"Run NRQL: SELECT count(*) FROM LlmEvent WHERE duration > 2 SINCE 1 hour ago"
Troubleshooting New Relic AI (LLM Observability) MCP Server with LlamaIndex
Common issues when connecting New Relic AI (LLM Observability) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNew Relic AI (LLM Observability) + LlamaIndex FAQ
Common questions about integrating New Relic AI (LLM Observability) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect New Relic AI (LLM Observability) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect New Relic AI (LLM Observability) to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.