Can my agent check the status of a specific Databricks job run?

Yes. Provide the 'job_id' to the 'list_job_runs' tool. The agent will retrieve the chronological history of executions, allowing you to identify successful completions or precise points of failure in your workflows.

How do I explore schemas within a specific Unity Catalog via chat?

Use the 'list_schemas' tool and provide the catalog name. Your agent will pull the detailed databases and schemas registered inside that Unity Catalog, giving you immediate visibility into your data hierarchy.

Can I monitor the health of my Databricks clusters through the agent?

Absolutely. The 'list_clusters' and 'get_cluster' tools allow your agent to retrieve detailed node information and operational statuses, helping you audit cluster health and capacity across your workspace.

Databricks MCP Server for OpenAI Agents SDK 8 tools — connect in under 2 minutes

Name: Databricks
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Databricks through the 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="Databricks Assistant",
            instructions=(
                "You help users interact with Databricks. "
                "You have access to 8 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Databricks"
        )
        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 Databricks MCP Server

Connect your Databricks workspace to any AI agent and take full control of your data intelligence platform and lakehouse orchestration through natural conversation.

The OpenAI Agents SDK auto-discovers all 8 tools from Databricks through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Databricks, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Cluster Monitoring — List all compute nodes and retrieve detailed information for specific clusters to audit health and capacity limits
Job Orchestration — List all configured workflows and jobs, and monitor recent executions to verify data pipeline statuses
SQL Warehouse Management — Enumerate explicitly configured SQL Serverless warehouses and track their active operational boundaries
Unity Catalog Exploration — List root catalogs and detailed schemas/databases to identify exactly where your structured data resides
Identity Oversight — Fetch profile information for the authenticated user or service principal to verify active workspace permissions
Run Auditing — Retrieve chronological logs of job runs to identify precise points of failure in your complex data workflows

The Databricks MCP Server exposes 8 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 Databricks to OpenAI Agents SDK via MCP

Follow these steps to integrate the Databricks 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 8 tools from Databricks

Why Use OpenAI Agents SDK with the Databricks MCP Server

OpenAI Agents SDK provides unique advantages when paired with Databricks 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

Databricks + OpenAI Agents SDK Use Cases

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

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

Multi-agent orchestration: create specialist agents — one queries Databricks, another analyzes results, a third generates reports

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

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

Databricks MCP Tools for OpenAI Agents SDK (8)

These 8 tools become available when you connect Databricks to OpenAI Agents SDK via MCP:

get_cluster

Get cluster details from Databricks

get_me

Get current user from Databricks

list_catalogs

List Unity Catalog catalogs from Databricks

list_clusters

List all clusters from Databricks

list_job_runs

List job runs from Databricks

list_jobs

List all jobs from Databricks

list_schemas

List schemas in catalog from Databricks

list_warehouses

List SQL warehouses from Databricks

Example Prompts for Databricks in OpenAI Agents SDK

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

"List all compute clusters in my workspace"

"Show me the last 5 runs for job 'Daily-Sales-ETL'"

"List all catalogs in Unity Catalog"

Troubleshooting Databricks MCP Server with OpenAI Agents SDK

Common issues when connecting Databricks 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.

Databricks + OpenAI Agents SDK FAQ

Common questions about integrating Databricks 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.