New Relic AI (LLM Observability) MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect New Relic AI (LLM Observability) through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="New Relic AI (LLM Observability) Assistant",
instructions=(
"You help users interact with New Relic AI (LLM Observability). "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from New Relic AI (LLM Observability)"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 10 tools from New Relic AI (LLM Observability) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries New Relic AI (LLM Observability), another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the New Relic AI (LLM Observability) MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from New Relic AI (LLM Observability)
Why Use OpenAI Agents SDK with the New Relic AI (LLM Observability) MCP Server
OpenAI Agents SDK provides unique advantages when paired with New Relic AI (LLM Observability) through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
New Relic AI (LLM Observability) + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the New Relic AI (LLM Observability) MCP Server delivers measurable value.
Automated workflows: build agents that query New Relic AI (LLM Observability), process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries New Relic AI (LLM Observability), another analyzes results, a third generates reports
Data enrichment pipelines: stream data through New Relic AI (LLM Observability) tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query New Relic AI (LLM Observability) to resolve tickets, look up records, and update statuses without human intervention
New Relic AI (LLM Observability) MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect New Relic AI (LLM Observability) to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting New Relic AI (LLM Observability) to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
New Relic AI (LLM Observability) + OpenAI Agents SDK FAQ
Common questions about integrating New Relic AI (LLM Observability) MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.