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 LangChain 8 tools — connect in under 2 minutes

Name: Databricks
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Databricks through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "databricks": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Databricks, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Ed25519Audit chain

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Databricks through native MCP adapters. Connect 8 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Databricks MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from Databricks via MCP

Why Use LangChain with the Databricks MCP Server

LangChain provides unique advantages when paired with Databricks through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Databricks MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Databricks queries for multi-turn workflows

Databricks + LangChain Use Cases

Practical scenarios where LangChain combined with the Databricks MCP Server delivers measurable value.

RAG with live data: combine Databricks tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Databricks, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Databricks tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Databricks tool call, measure latency, and optimize your agent's performance

Databricks MCP Tools for LangChain (8)

These 8 tools become available when you connect Databricks to LangChain 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 LangChain

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

Common issues when connecting Databricks to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Databricks + LangChain FAQ

Common questions about integrating Databricks MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.