Databricks Workspace

Live

BEARER TOKEN

ANALYTICS

Analytics

Every notebook, cluster, and SQL job your team runs lives in Databricks. Databricks Workspace MCP gives your agent authenticated access to your data platform scoped to the user who authorized it.

  • Acts as the user: Access and write actions stay tied to the Databricks Workspace account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: User permissions enforced. 90-day audit trail.
Databricks Workspace
agent · Acme Q3
Run
Which jobs failed in the last 24 hours and what were the error messages?
S
databricks_jobs_list
91ms
Data platform agent
3 failed jobs. etl_daily_pipeline (OOM: 32GB limit exceeded), feature_store_refresh (timeout 3600s), clickstream_agg (syntax error in stage 3).
Sources: 3 failed job runs, last 24 hours
databricksworkspacemcp
3 jobs
18:29
Message Claude...

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.
databricksworkspace_cluster_create
Create cluster
Create and start a new Databricks compute cluster. Specify either a fixed number of workers or an autoscaling range.
Parameters
Name
Type
Required
Description
cluster_name
string
Required
Display name for the new cluster
node_type_id
string
Required
The instance type for driver and worker nodes
spark_version
string
Required
The Databricks Runtime version to use for the cluster
autotermination_minutes
integer
Optional
Automatically terminate the cluster after this many minutes of inactivity. Set to 0 to disable.
max_workers
integer
Optional
Maximum number of workers when autoscaling. Provide with min_workers instead of num_workers.
min_workers
integer
Optional
Minimum number of workers when autoscaling. Provide with max_workers instead of num_workers.
num_workers
integer
Optional
Fixed number of worker nodes. Omit and use min_workers/max_workers instead to enable autoscaling.
databricksworkspace_cluster_edit
Cluster edit
databricksworkspace_cluster_get
Get cluster
databricksworkspace_cluster_permanent_delete
Delete cluster permanent
databricksworkspace_cluster_resize
Cluster resize
databricksworkspace_cluster_restart
Cluster restart
databricksworkspace_cluster_start
Start cluster
databricksworkspace_clusters_list
List clusters
databricksworkspace_dbfs_put
Dbfs put
databricksworkspace_dbfs_read
Read dbfs
databricksworkspace_information_schema_schemata
Information schema schemata
databricksworkspace_information_schema_tables
Information schema tables
databricksworkspace_job_delete
Delete job
databricksworkspace_job_get
Get job
databricksworkspace_job_run_now
Job run now
databricksworkspace_job_runs_list
List job runs
databricksworkspace_permissions_get
Get permissions
databricksworkspace_permissions_update
Update permissions
databricksworkspace_repo_create
Create repo
databricksworkspace_repos_list
List repos
databricksworkspace_secret_delete
Delete secret
databricksworkspace_secrets_list
List secrets
databricksworkspace_sql_statement_cancel
Cancel sql statement
databricksworkspace_sql_statement_execute
Execute sql statement
databricksworkspace_sql_statement_get
Get sql statement
databricksworkspace_sql_warehouse_get
Get sql warehouse
databricksworkspace_sql_warehouse_stop
Stop sql warehouse
databricksworkspace_sql_warehouses_list
List sql warehouses
databricksworkspace_unity_catalog_schemas_list
List unity catalog schemas
databricksworkspace_workspace_export
Export workspace

For more tools, view docs.

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.
Python · LlamaIndex
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 });
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 }],
});
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
Copied
List all running clusters.
Copy the prompt
Copied
Show me notebooks in /Shared/pipelines.
Copy the prompt
Copied
Which jobs are scheduled to run tonight?
Copy the prompt
Copied
Get the schema for [table name] in [catalog].
Query & analysis
Copy the prompt
Copied
Run: SELECT date, COUNT(*) FROM events.signups GROUP BY date.
Copy the prompt
Copied
Top 10 tables by row count in [schema].
Copy the prompt
Copied
Daily job run counts for the last 14 days.
Copy the prompt
Copied
Failed job error summary this week.
Monitoring & ops
Copy the prompt
Copied
Which jobs failed in the last 24 hours?
Copy the prompt
Copied
Get error message for run [run_id].
Copy the prompt
Copied
List clusters with more than 10 workers.
Copy the prompt
Copied
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.
1
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
2
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
3
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
4
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.
GTM and RevOps Teams
Revenue forecast commentary
Pulls open pipeline from Salesforce and HubSpot, calculates coverage against quota, flags at-risk stages, posts commentary to Slack, and logs every snapshot to Google Sheets.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
Engineering Teams
Engineering standup agent
Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
Test other agents
Same per-user auth pattern across other data platform agents and MCP connectors. Working code, live demos, fork what fits.
ENGINEERING
Engineering standup agent
Pull commits from GitHub and GitLab, track Jira issue movement, and post a per-engineer standup brief to Slack.
GTM
Revenue forecast agent
Score pipeline coverage against quota across Salesforce and HubSpot, post forecast commentary to Slack, log snapshots to Sheets.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
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
FAQs
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?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.
Can I limit what the agent is allowed to do in Databricks Workspace?
Yes. Pass a tool name filter to listScopedTools so the data platform agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Databricks Workspace.
What happens when a user revokes Databricks Workspace access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.
Can the agent run SQL queries across all Unity Catalog catalogs?
Only catalogs and schemas the authorizing user's personal access token permits. Unity Catalog permissions, table ACLs, and column-level masking all apply. Queries that exceed the user's grants return an access denied error, not partial data.
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
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""databricksworkspace"": {
""url"": ""https://mcp.scalekit.com/databricksworkspace"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.databricksworkspace]
url = ""https://mcp.scalekit.com/databricksworkspace""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""databricksworkspace"": {
""url"": ""https://mcp.scalekit.com/databricksworkspace"",
""type"": ""http""
}
}
}