Databricks MCP Server

A comprehensive Model Context Protocol (MCP) server for Databricks, built on the official Databricks Python SDK.
Provides 263 tools and 8 prompt templates across 28 service domains, giving AI assistants full access to the Databricks platform.
Features
- SDK-first: Uses
databricks-sdk for type safety and automatic API freshness
- Comprehensive: Covers Unity Catalog, SQL, Compute, Jobs, Pipelines, Serving, Vector Search, Apps, Lakebase, Dashboards, Genie, Secrets, IAM, Connections, Experiments, and Delta Sharing
- Zero custom auth: Delegates authentication entirely to the SDK (PAT, OAuth, Azure AD, service principal -- all automatic)
- Selective loading: Include/exclude tool modules via environment variables
- MCP Resources: Read-only workspace context (URL, current user, auth type)
Quick Start
Installation
pip install databricks-sdk-mcp
Or run with Docker:
docker run -i -e DATABRICKS_HOST=... -e DATABRICKS_TOKEN=... databricks-mcp
Or install from source:
git clone https://github.com/pramodbhatofficial/databricks-mcp-server.git
cd databricks-mcp-server
pip install -e ".[dev]"
Authentication
Authentication is handled by the Databricks SDK. Set one of:
Personal Access Token (simplest):
export DATABRICKS_HOST=https://your-workspace.databricks.com
export DATABRICKS_TOKEN=dapi...
OAuth (M2M):
export DATABRICKS_HOST=https://your-workspace.databricks.com
export DATABRICKS_CLIENT_ID=...
export DATABRICKS_CLIENT_SECRET=...
Other methods: Azure AD, Databricks CLI profile, Azure Managed Identity -- all auto-detected by the SDK.
Running
This starts the MCP server using stdio transport.
Integrations
Claude Code (Terminal)
Add to ~/.claude/settings.json or your project's .claude/settings.json:
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
Then restart Claude Code. Verify with /mcp to see the registered tools.
Claude Desktop
Add to your Claude Desktop config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
- Windows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
Restart Claude Desktop. The Databricks tools will appear in the tool picker.
Cursor
Add to .cursor/mcp.json in your project root (or ~/.cursor/mcp.json for global):
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
Open Cursor Settings > MCP to verify the server is connected.
Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
VS Code (Copilot)
Add to .vscode/mcp.json in your project:
{
"servers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
Zed
Add to Zed's settings (~/.config/zed/settings.json):
{
"context_servers": {
"databricks": {
"command": {
"path": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi..."
}
}
}
}
}
Any MCP Client (Generic stdio)
The server uses stdio transport. Connect from any MCP-compatible client:
export DATABRICKS_HOST=https://your-workspace.databricks.com
export DATABRICKS_TOKEN=dapi...
databricks-mcp
Tip: Load Only What You Need
If your MCP client struggles with many tools, use selective loading to reduce the tool count:
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi...",
"DATABRICKS_MCP_TOOLS_INCLUDE": "unity_catalog,sql,compute,jobs"
}
}
}
}
| Module | Tools | Description |
|---|
unity_catalog | 23 | Catalogs, schemas, tables, volumes, functions, registered models |
sql | 14 | Warehouses, SQL execution, queries, alerts, history |
workspace | 10 | Notebooks, files, repos |
compute | 18 | Clusters, instance pools, policies, node types, Spark versions |
jobs | 13 | Jobs, runs, tasks, repair, cancel all |
pipelines | 8 | DLT / Lakeflow pipelines |
serving | 10 | Serving endpoints, model versions, OpenAPI |
vector_search | 10 | Vector search endpoints, indexes, sync |
apps | 10 | Databricks Apps lifecycle |
database | 10 | Lakebase PostgreSQL instances |
dashboards | 9 | Lakeview AI/BI dashboards, published views |
genie | 5 | Genie AI/BI conversations |
secrets | 8 | Secret scopes and secrets |
iam | 16 | Users, groups, service principals, permissions, current user |
connections | 5 | External connections |
experiments | 14 | MLflow experiments, runs, artifacts, metrics, params |
sharing | 11 | Delta Sharing shares, recipients, providers |
files | 12 | DBFS and UC Volumes file operations |
grants | 3 | Unity Catalog permission grants (GRANT/REVOKE) |
storage | 10 | Storage credentials and external locations |
metastores | 8 | Unity Catalog metastore management |
online_tables | 3 | Online tables for low-latency serving |
global_init_scripts | 5 | Workspace-wide init scripts |
tokens | 5 | Personal access token management |
git_credentials | 5 | Git credential management for repos |
quality_monitors | 8 | Data quality monitoring and refreshes |
command_execution | 4 | Interactive command execution on clusters |
workflows | 5 | Composite multi-step operations (workspace status, schema setup, query preview) |
With 263 tools, it's recommended to load only the modules you need. This improves agent performance and tool selection accuracy.
Role-Based Presets (Recommended)
Pick a preset that matches your role:
| Preset | Modules | Tools | Config |
|---|
| Data Engineer | unity_catalog, sql, compute, jobs, pipelines, files, quality_monitors | ~100 | DATABRICKS_MCP_TOOLS_INCLUDE=unity_catalog,sql,compute,jobs,pipelines,files,quality_monitors |
| ML Engineer | serving, vector_search, experiments, compute, unity_catalog, online_tables, files | ~98 | DATABRICKS_MCP_TOOLS_INCLUDE=serving,vector_search,experiments,compute,unity_catalog,online_tables,files |
| Platform Admin | iam, secrets, tokens, metastores, compute, global_init_scripts, grants, storage | ~85 | DATABRICKS_MCP_TOOLS_INCLUDE=iam,secrets,tokens,metastores,compute,global_init_scripts,grants,storage |
| App Developer | apps, database, sql, files, serving, secrets | ~64 | DATABRICKS_MCP_TOOLS_INCLUDE=apps,database,sql,files,serving,secrets |
| Data Analyst | sql, unity_catalog, dashboards, genie, workspace | ~61 | DATABRICKS_MCP_TOOLS_INCLUDE=sql,unity_catalog,dashboards,genie,workspace |
| Minimal | sql, unity_catalog | ~37 | DATABRICKS_MCP_TOOLS_INCLUDE=sql,unity_catalog |
Example using a preset in Claude Code:
{
"mcpServers": {
"databricks": {
"command": "databricks-mcp",
"env": {
"DATABRICKS_HOST": "https://your-workspace.databricks.com",
"DATABRICKS_TOKEN": "dapi...",
"DATABRICKS_MCP_TOOLS_INCLUDE": "unity_catalog,sql,compute,jobs,pipelines,files,quality_monitors"
}
}
}
}
Custom Filtering
export DATABRICKS_MCP_TOOLS_INCLUDE=unity_catalog,sql,serving
export DATABRICKS_MCP_TOOLS_EXCLUDE=iam,sharing,experiments
If INCLUDE is set, only those modules load. If EXCLUDE is set, everything except those modules loads. INCLUDE takes precedence if both are set.
The server includes built-in tool discovery to help AI agents find the right tools:
MCP Resources
| URI | Description |
|---|
databricks://workspace/info | Workspace URL, current user, auth type |
databricks://tools/guide | Tool catalog with module descriptions, use cases, and role presets |
Agents can read databricks://tools/guide at connection time to understand what's available.
The databricks_tool_guide tool helps agents find the right tools during a conversation:
# Find tools for a specific task
databricks_tool_guide(task="run a SQL query")
databricks_tool_guide(task="deploy an ML model")
databricks_tool_guide(task="create a user")
# Get role-based recommendations
databricks_tool_guide(role="data_engineer")
databricks_tool_guide(role="ml_engineer")
This returns matching modules with descriptions and usage hints, so the agent knows exactly which databricks_* tools to call.
MCP Prompts (Guided Workflows)
The server includes 8 prompt templates that guide AI agents through multi-step Databricks workflows:
| Prompt | Description |
|---|
explore_data_catalog | Browse Unity Catalog structure (catalogs → schemas → tables) |
query_data | Find a warehouse, execute SQL, and format results |
debug_failing_job | Investigate a failing job: status, logs, error analysis |
setup_ml_experiment | Create an MLflow experiment and configure tracking |
deploy_model | Deploy a model to a serving endpoint |
setup_data_pipeline | Create a DLT pipeline with scheduling |
workspace_health_check | Audit clusters, warehouses, jobs, and endpoints |
manage_permissions | Review and update permissions on workspace objects |
Prompts appear automatically in MCP clients that support them (e.g., Claude Desktop's prompt picker).
Docker
Run the MCP server in a container:
docker build -t databricks-mcp .
docker run -i \
-e DATABRICKS_HOST=https://your-workspace.databricks.com \
-e DATABRICKS_TOKEN=dapi... \
databricks-mcp
docker run -p 8080:8080 \
-e DATABRICKS_HOST=https://your-workspace.databricks.com \
-e DATABRICKS_TOKEN=dapi... \
databricks-mcp --transport sse --port 8080
docker run -i \
-e DATABRICKS_HOST=https://your-workspace.databricks.com \
-e DATABRICKS_TOKEN=dapi... \
-e DATABRICKS_MCP_TOOLS_INCLUDE=sql,unity_catalog \
databricks-mcp
SSE Transport (Remote Server)
The server supports SSE transport for remote connections:
databricks-mcp --transport sse --port 8080
databricks-mcp --transport sse --host 127.0.0.1 --port 3000
Connect from any MCP client that supports SSE:
{
"mcpServers": {
"databricks": {
"url": "http://localhost:8080/sse"
}
}
}
Development
pip install -e ".[dev]"
ruff check databricks_mcp/
pytest tests/ -v
Author
Pramod Bhat
License
Apache 2.0 -- see LICENSE.