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

Q: How do I integrate New Relic AI (LLM Observability) with Pydantic AI?

Import the MCPToolset from the pydantic-ai-slim package. Add your endpoint to the toolset and pass it to your agent for immediate type-safe access.

Use type-safe monitoring for Pydantic AI agents to ensure your production telemetry is always valid.

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Connect New Relic AI (LLM Observability) MCP to Pydantic AI

Create your Vinkius account to connect New Relic AI (LLM Observability) to Pydantic AI 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 Pydantic AI

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Validate telemetry in Pydantic AI

Ensure your agent only acts on accurate data. Every call to `query_llm_costs` or `query_llm_errors` is validated against your schema at runtime. If the telemetry data is malformed, your agent stops immediately. You avoid the risk of building logic on corrupted or hallucinated monitoring values.

Query dashboards in Pydantic AI

Give your agent a view of your entire monitoring setup. Use `list_dashboards` to find the right environment for your current task. Your agent can now programmatically find the correct dashboard to report its status. It keeps your monitoring documentation up to date automatically.

Read custom telemetry in Pydantic AI

Use `query_llm_events` to pull specific records from your platform. This lets your agent filter through historical data with strict type checking. This makes your agents highly reliable when they need to report on past performance. It enforces a strict contract between your telemetry and your agent's logic.

Setup guide

Set up New Relic AI (LLM Observability) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "new-relic-ai-llm-observability-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to New Relic AI (LLM Observability) tools.",
)

result = await agent.run("List recent New Relic AI (LLM Observability) transactions")
print(result.output)

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

Import the `MCPToolset` from the pydantic-ai-slim package. Add your endpoint to the toolset and pass it to your agent for immediate type-safe access.

It does. The toolset validates all incoming responses from the server against your Pydantic models, preventing runtime errors.

Yes. Because Pydantic AI validates every response, the agent will throw an error if the telemetry data doesn't match your expected types.

The server supports both SSE and Streamable HTTP. You can choose the transport that fits your deployment architecture best.

The server operates in a zero-trust sandbox. It only exposes the specific telemetry fields you request, ensuring no sensitive system logs are leaked.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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