Databricks Workspace MCP for AI agents

Tools your data platform agent reaches for on Databricks Workspace, scoped per user.

CALL ANY TOOL

List clusters and jobs, run SQL queries, inspect notebook paths, and monitor run status.

databricks_clusters_list

List clusters

List all Databricks clusters with state, driver, and runtime version.

Parameters

Name

Type

Required

Description

filter_by_creator

string

Optional

Filter by creator username

databricks_cluster_get

Get cluster

databricks_jobs_list

List jobs

databricks_job_run_get

Get job run

databricks_sql_execute

Execute SQL

databricks_notebooks_list

List notebooks

Build your Agent

Drop the toolkit in, point it at the user, and your data platform agent can use Databricks Workspace from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["databricksworkspace"], toolNames: ["databricks_clusters_list", "databricks_cluster_get", "databricks_jobs_list"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["databricksworkspace"], toolNames: ["databricks_clusters_list", "databricks_cluster_get", "databricks_jobs_list"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["databricksworkspace"], toolNames: ["databricks_clusters_list", "databricks_cluster_get", "databricks_jobs_list"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/databricksworkspace",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using Databricks Workspace.

Search & schema

Copy the prompt

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List all running clusters.

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Show me notebooks in /Shared/pipelines.

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Which jobs are scheduled to run tonight?

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Get the schema for [table name] in [catalog].

Query & analysis

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Run: SELECT date, COUNT(*) FROM events.signups GROUP BY date.

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Top 10 tables by row count in [schema].

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Daily job run counts for the last 14 days.

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Failed job error summary this week.

Monitoring & ops

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Which jobs failed in the last 24 hours?

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Get error message for run [run_id].

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List clusters with more than 10 workers.

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Which SQL warehouses are auto-stopped?

SEE HOW AUTH WORKS

Users authorize Databricks Workspace once. Their credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Databricks Workspace

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

Databricks Workspace

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Databricks Workspace

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Databricks Workspace

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other data platform agents and MCP connectors. Working code, live demos, fork what fits.

ENGINEERING

DevOps assistant agent

Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Queries run as the service principal, not the analyst

A shared Databricks PAT looks fine in a demo. In production, every SQL query and job run is attributed to the service account. Unity Catalog row filters break. Scalekit resolves the user's token so queries run as the right identity.

// shared PAT
token = "dapi_databricks_shared_xxx"
audit → svc_account
uc_filter → broken

// scalekit · per-user
token = resolve(user_id)
audit → user_abc
scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Databricks Workspace today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit

Frequently Asked Questions

Does the agent access Databricks Workspace as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Databricks Workspace bearer token stored?

Can I limit what the agent is allowed to do in Databricks Workspace?

What happens when a user revokes Databricks Workspace access?

Can the agent run SQL queries across all Unity Catalog catalogs?

Start in your coding agent

Up and running in one command

Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up

Claude Code REPL

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/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack

Cursor Code REPL

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# ~/.cursor/mcp.json
{
""mcpServers"": {
""databricksworkspace"": {
""url"": ""https://mcp.scalekit.com/databricksworkspace"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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# ~/.codex/config.toml
[mcp_servers.databricksworkspace]
url = ""https://mcp.scalekit.com/databricksworkspace""
auth_env = ""SCALEKIT_TOKEN""

Copilot Code REPL

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# .vscode/mcp.json
{
""servers"": {
""databricksworkspace"": {
""url"": ""https://mcp.scalekit.com/databricksworkspace"",
""type"": ""http""
}
}
}