Databricks MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Databricks as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Databricks. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Databricks?"
)
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 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.
LlamaIndex agents combine Databricks tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the 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
- 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 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 Databricks to LlamaIndex via MCP
Follow these steps to integrate the Databricks 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 8 tools from Databricks
Why Use LlamaIndex with the Databricks MCP Server
LlamaIndex provides unique advantages when paired with Databricks through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Databricks tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Databricks tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Databricks, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Databricks tools were called, what data was returned, and how it influenced the final answer
Databricks + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Databricks MCP Server delivers measurable value.
Hybrid search: combine Databricks real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Databricks 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 Databricks for fresh data
Analytical workflows: chain Databricks queries with LlamaIndex's data connectors to build multi-source analytical reports
Databricks MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Databricks to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Databricks to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDatabricks + LlamaIndex FAQ
Common questions about integrating Databricks MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Databricks with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Databricks to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.