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 LlamaIndex 10 tools — connect in under 2 minutes

Name: Arize AI
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Arize AI as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Arize AI. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Arize AI?"
    )
    print(response)

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.

LlamaIndex agents combine Arize AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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

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 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 Arize AI to LlamaIndex via MCP

Follow these steps to integrate the Arize AI 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 Arize AI

Why Use LlamaIndex with the Arize AI MCP Server

LlamaIndex provides unique advantages when paired with Arize AI through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Arize AI tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Arize AI tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Arize AI, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Arize AI tools were called, what data was returned, and how it influenced the final answer

Arize AI + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Arize AI MCP Server delivers measurable value.

Hybrid search: combine Arize AI real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Arize AI 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 Arize AI for fresh data

Analytical workflows: chain Arize AI queries with LlamaIndex's data connectors to build multi-source analytical reports

Arize AI MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Arize AI to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Arize AI + LlamaIndex FAQ

Common questions about integrating Arize AI MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Arize AI tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.