Databricks Genie

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

manage_genie - Space Management

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_count

4. 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 config

Clone 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, then manage_genie(action="export") from the source server and manage_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:

1. Tables in Unity Catalog - Bronze/silver/gold tables with the data
2. SQL Warehouse - A warehouse to execute queries (auto-detected if not specified)

Creating Tables

Use these skills in sequence:

1. databricks-synthetic-data-gen - Generate raw parquet files
2. databricks-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