Databricks Genie
Create, manage, and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration.
Overview
Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally.
When to Use This Skill
Use this skill when:
- Creating a new Genie Space for data exploration
- Adding sample questions to guide users
- Connecting Unity Catalog tables to a conversational interface
- Asking questions to a Genie Space programmatically (Conversation API)
- Exporting a Genie Space configuration (serialized_space) for backup or migration
- Importing / cloning a Genie Space from a serialized payload
- Migrating a Genie Space between workspaces or environments (dev → staging → prod)
- Only supports catalog remapping where catalog names differ across environments - Not supported for schema and/or table names that differ across environments - Not including migration of tables between environments (only migration of Genie Spaces)
MCP Tools
| Tool | Purpose |
|---|---|
manage_genie | Create, get, list, delete, export, and import Genie Spaces |
ask_genie | Ask natural language questions to a Genie Space |
get_table_stats_and_schema | Inspect table schemas before creating a space |
execute_sql | Test SQL queries directly |
manage_genie - Space Management
| Action | Description | Required Params |
|---|---|---|
create_or_update | Idempotent create/update a space | display_name, table_identifiers (or serialized_space) |
get | Get space details | space_id |
list | List all spaces | (none) |
delete | Delete a space | space_id |
export | Export space config for migration/backup | space_id |
import | Import space from serialized config | warehouse_id, serialized_space |
Example tool calls:
# MCP Tool: manage_genie
# Create a new space
manage_genie(
action="create_or_update",
display_name="Sales Analytics",
table_identifiers=["catalog.schema.customers", "catalog.schema.orders"],
description="Explore sales data with natural language",
sample_questions=["What were total sales last month?"]
)
# MCP Tool: manage_genie
# Get space details with full config
manage_genie(action="get", space_id="space_123", include_serialized_space=True)
# MCP Tool: manage_genie
# List all spaces
manage_genie(action="list")
# MCP Tool: manage_genie
# Export for migration
exported = manage_genie(action="export", space_id="space_123")
# MCP Tool: manage_genie
# Import to new workspace
manage_genie(
action="import",
warehouse_id="warehouse_456",
serialized_space=exported["serialized_space"],
title="Sales Analytics (Prod)"
)ask_genie - Conversation API (Query)
Ask natural language questions to a Genie Space. Pass conversation_id for follow-up questions.
# MCP Tool: ask_genie
# Start a new conversation
result = ask_genie(
space_id="space_123",
question="What were total sales last month?"
)
# Returns: {question, conversation_id, message_id, status, sql, columns, data, row_count}
# MCP Tool: ask_genie
# Follow-up question in same conversation
result = ask_genie(
space_id="space_123",
question="Break that down by region",
conversation_id=result["conversation_id"]
)Quick Start
1. Inspect Your Tables
Before creating a Genie Space, understand your data:
# MCP Tool: get_table_stats_and_schema
get_table_stats_and_schema(
catalog="my_catalog",
schema="sales",
table_stat_level="SIMPLE"
)2. Create the Genie Space
# MCP Tool: manage_genie
manage_genie(
action="create_or_update",
display_name="Sales Analytics",
table_identifiers=[
"my_catalog.sales.customers",
"my_catalog.sales.orders"
],
description="Explore sales data with natural language",
sample_questions=[
"What were total sales last month?",
"Who are our top 10 customers?"
]
)3. Ask Questions (Conversation API)
# MCP Tool: ask_genie
ask_genie(
space_id="your_space_id",
question="What were total sales last month?"
)
# Returns: SQL, columns, data, row_count4. Export & Import (Clone / Migrate)
Export a space (preserves all tables, instructions, SQL examples, and layout):
# MCP Tool: manage_genie
exported = manage_genie(action="export", space_id="your_space_id")
# exported["serialized_space"] contains the full configClone to a new space (same catalog):
# MCP Tool: manage_genie
manage_genie(
action="import",
warehouse_id=exported["warehouse_id"],
serialized_space=exported["serialized_space"],
title=exported["title"], # override title; omit to keep original
description=exported["description"],
)Cross-workspace migration: Each MCP server is workspace-scoped. Configure one server entry per workspace profile in your IDE's MCP config, thenmanage_genie(action="export")from the source server andmanage_genie(action="import")via the target server. See spaces.md §Migration for the full workflow.
Reference Files
- spaces.md - Creating and managing Genie Spaces
- conversation.md - Asking questions via the Conversation API
Prerequisites
Before creating a Genie Space:
- Tables in Unity Catalog - Bronze/silver/gold tables with the data
- SQL Warehouse - A warehouse to execute queries (auto-detected if not specified)
Creating Tables
Use these skills in sequence:
databricks-synthetic-data-gen- Generate raw parquet filesdatabricks-spark-declarative-pipelines- Create bronze/silver/gold tables
Common Issues
See spaces.md §Troubleshooting for a full list of issues and solutions.
Related Skills
- databricks-agent-bricks - Use Genie Spaces as agents inside Supervisor Agents
- databricks-synthetic-data-gen - Generate raw parquet data to populate tables for Genie
- databricks-spark-declarative-pipelines - Build bronze/silver/gold tables consumed by Genie Spaces
- databricks-unity-catalog - Manage the catalogs, schemas, and tables Genie queries