Can my AI automatically trigger a hallucination evaluation on a new dataset?

Yes! You can ask your agent to retrieve the specific Ground Truth dataset ID, formulate a testing payload, and invoke the `run_eval` tool natively. Arize will process the asynchronous scoring internally and log the evaluation securely.

How can I quickly check if a production model is experiencing data drift?

Just tell your agent: 'Fetch the primary metrics for model X'. The AI uses the `get_metrics` query to immediately surface latency degradation, prediction drift flags, and incoming data quality indexes without opening the browser.

Is it possible to track telemetry simultaneously for both local development and production environments?

Absolutely. Arize enforces strict separation using Spaces and Environments. You can instruct your AI agent to query the `list_environments` tool, figure out the sandbox ID, and push manual test logs strictly to the sandbox scope during debugging sessions, keeping production metrics clean.

Arize AI MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Arize AI
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Arize AI through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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="Arize AI Assistant",
            instructions=(
                "You help users interact with Arize AI. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Arize AI"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Arize AI MCP Server

Connect your Arize AI observability platform to any AI agent and take full control of your Machine Learning and LLM telemetry workflows through natural conversation.

The OpenAI Agents SDK auto-discovers all 10 tools from Arize AI through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Arize AI, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Model Monitoring & Metrics — List all tracked ML models, extract deep configuration schemas, and fetch real-time metrics (performance, data quality, and prediction drift)
Evaluation & Alignment — Launch and list automated LLM evaluation runs (e.g., Toxicity, Hallucination, PII filtering) against static datasets and ground truth baselines
Telemetry Ingestion — Push programmatic raw logs, predictions, and inferences straight into Arize for immediate visualization and tracking
Space & Environment Management — Browse organizational spaces and segregated deployment environments (Production, Training, Verification)

The Arize AI 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 Arize AI to OpenAI Agents SDK via MCP

Follow these steps to integrate the Arize AI 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 Arize AI

Why Use OpenAI Agents SDK with the Arize AI MCP Server

OpenAI Agents SDK provides unique advantages when paired with Arize AI 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

Arize AI + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Arize AI MCP Server delivers measurable value.

Automated workflows: build agents that query Arize AI, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Arize AI, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Arize AI tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Arize AI to resolve tickets, look up records, and update statuses without human intervention

Arize AI MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Arize AI to OpenAI Agents SDK via MCP:

get_dataset

Get a specific evaluation dataset

get_metrics

Fetch observability metrics for an ML model

get_model

It defines the inputs, outputs, and features. Get details and metadata for a specific tracked model

ingest_log

payload_json must contain valid Arize payload structures. Ingest raw telemetry logs into Arize

list_datasets

List static evaluation datasets

list_environments

g., Production, Training, Verification) used to segregate model inferences and baseline datasets. List configured environments within Arize

list_evals

g., Toxicity, Hallucination, PII filtering). List automated evaluation runs

list_models

List tracked ML models or LLMs

list_spaces

Spaces separate different models and telemetry datasets. List accessible workspaces within the Arize platform

run_eval

Trigger a custom LLM evaluation run

Example Prompts for Arize AI in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Arize AI immediately.

"List all active Machine Learning models monitored in my workspace."

"Get the evaluation baseline datasets available for our LLM checks."

"Push these 3 mocked prompt responses as telemetry logs to the 'OpenAI-Customer-Service-Bot' model."

Troubleshooting Arize AI MCP Server with OpenAI Agents SDK

Common issues when connecting Arize AI to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Arize AI + OpenAI Agents SDK FAQ

Common questions about integrating Arize AI MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.