Arize AI MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Dataset, Get Model, List Datasets, and more
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.
Ask AI about this App Connector for LlamaIndex
The Arize AI app connector for LlamaIndex is a standout in the Friends Mcp category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 6 tools available."
),
)
response = await agent.run(
"What tools are available in Arize AI?"
)
print(response)
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 Arize AI MCP Server
Connect your Arize AI account to any AI agent and take full control of your machine learning observability and automated model monitoring workflows through natural conversation.
LlamaIndex agents combine Arize AI tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Project & Trace Orchestration — List and monitor active ML tracing projects programmatically, retrieving detailed high-fidelity execution spans and telemetry data in real-time
- Dataset Lifecycle Management — Programmatically create and manage datasets for model evaluation and validation to maintain a perfectly coordinated ML infrastructure
- Experiment Monitoring — Access and track ML experiments to understand high-fidelity model performance, drift, and data quality across different environments
- Model Intelligence Discovery — Retrieve detailed metadata for specific ML models to coordinate your organizational AI strategy directly through your agent
- Operational Monitoring — Access account-level settings and verify API connectivity directly through your agent for instant performance reporting
The Arize AI MCP Server exposes 6 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.
All 6 Arize AI tools available for LlamaIndex
When LlamaIndex connects to Arize AI through Vinkius, your AI agent gets direct access to every tool listed below — spanning ml-observability, model-monitoring, data-drift, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a dataset
Get model details
List datasets
List experiments
List projects
List spans
Connect Arize AI to LlamaIndex via MCP
Follow these steps to wire Arize AI into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
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
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 ML projects in my Arize account."
"Show the recent execution spans for project '1024'."
"Create a new dataset 'Q2_Eval_Data' for model evaluation."
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
pip install llama-index-tools-mcpArize AI + LlamaIndex FAQ
Common questions about integrating Arize AI MCP Server with LlamaIndex.