New Relic AI (LLM Observability) MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect New Relic AI (LLM Observability) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to New Relic AI (LLM Observability) "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in New Relic AI (LLM Observability)?"
)
print(result.data)
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.
Pydantic AI validates every New Relic AI (LLM Observability) tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the New Relic AI (LLM Observability) MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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) with type-safe schemas
Why Use Pydantic AI with the New Relic AI (LLM Observability) MCP Server
Pydantic AI provides unique advantages when paired with New Relic AI (LLM Observability) through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your New Relic AI (LLM Observability) integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your New Relic AI (LLM Observability) connection logic from agent behavior for testable, maintainable code
New Relic AI (LLM Observability) + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the New Relic AI (LLM Observability) MCP Server delivers measurable value.
Type-safe data pipelines: query New Relic AI (LLM Observability) with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple New Relic AI (LLM Observability) tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query New Relic AI (LLM Observability) and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock New Relic AI (LLM Observability) responses and write comprehensive agent tests
New Relic AI (LLM Observability) MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect New Relic AI (LLM Observability) to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting New Relic AI (LLM Observability) to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiNew Relic AI (LLM Observability) + Pydantic AI FAQ
Common questions about integrating New Relic AI (LLM Observability) MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.