How to Use the Databricks MCP in LlamaIndex

Q: How can LlamaIndex index my Databricks cluster configurations?

You configure your LlamaIndex agent to call the listclusters tool. The agent ingests the tool's output and adds it to a vector index, making your cluster data queryable in natural language.

Q: Can I build a RAG app over my Databricks Unity Catalog using LlamaIndex?

Yes, that's a primary use case. Use the listcatalogs and listschemas tools to feed your catalog structure into a LlamaIndex RAG pipeline. Your agent can then answer questions about your data assets.

Q: What's the process to give a LlamaIndex agent access to Databricks tools?

First, install the llama-index-tools-mcp package. Then, create an McpToolSpec with your Vinkius endpoint and pass the generated tools to your FunctionAgent. It's designed to be straightforward.

Create a searchable knowledge base of your live Databricks environment using LlamaIndex.

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Create your Vinkius account to connect Databricks to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Databricks with LlamaIndex

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Turn Cluster State into a Queryable Index

Use the `list_clusters` and `list_job_runs` tools to pull down the current state of your Databricks compute. LlamaIndex doesn't just show you the raw JSON; it indexes the output, turning it into a structured knowledge base. Now your agent can answer natural language questions grounded in fact. Ask "Which clusters are running Spark 3.4?" or "What jobs failed this morning?" and LlamaIndex will find the answer from the indexed tool outputs, preventing your agent from hallucinating about your infrastructure.

Build a RAG Agent for Unity Catalog with LlamaIndex

This MCP Server connects LlamaIndex directly to your Unity Catalog's structure. Your agent can run `list_catalogs` and `list_schemas` on a schedule, continuously updating a vector index of your entire data layout. This gives you a RAG application that actually knows your data estate. A data analyst can ask "What schemas are under the 'finance' catalog?" and get an accurate answer based on the indexed metadata. Your agent's knowledge stays current with your Databricks environment.

Index and Query Job & Warehouse Configs

Point LlamaIndex at the `list_jobs` and `list_warehouses` tools. It will fetch the configurations and statuses, then embed them into its searchable index. This creates a snapshot of your operational setup. This lets you build agents that answer questions about your configuration. "How many SQL warehouses are running?" or "Show me the schedule for the 'nightly_report' job." LlamaIndex retrieves the answer from its knowledge base, which is built from real data pulled via MCP tool calls.

Setup guide

Set up Databricks MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Databricks MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Databricks tools.",
)
response = await agent.run("List recent Databricks data")

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Common questions about Databricks MCP in LlamaIndex

You configure your LlamaIndex agent to call the `list_clusters` tool. The agent ingests the tool's output and adds it to a vector index, making your cluster data queryable in natural language.

Yes, that's a primary use case. Use the `list_catalogs` and `list_schemas` tools to feed your catalog structure into a LlamaIndex RAG pipeline. Your agent can then answer questions about your data assets.

First, install the `llama-index-tools-mcp` package. Then, create an `McpToolSpec` with your Vinkius endpoint and pass the generated tools to your FunctionAgent. It's designed to be straightforward.

You control the updates. You can configure your LlamaIndex application to run the Databricks tools on a schedule (e.g., every hour) to refresh the index and keep it current.

Your agent will access metadata like catalog definitions, schema names, and job configurations. Vinkius operates on a zero-trust model, where your single endpoint token grants limited, temporary access. The platform handles the credential exchange so your keys are never exposed.

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