Databricks Docs

Databricks Documentation Reference

This skill provides access to the complete Databricks documentation index via llms.txt - use it as a reference resource to supplement other skills and inform your use of MCP tools.

Role of This Skill

This is a reference skill, not an action skill. Use it to:

• Look up documentation when other skills don't cover a topic
• Get authoritative guidance on Databricks concepts and APIs
• Find detailed information to inform how you use MCP tools
• Discover features and capabilities you may not know about

Always prefer using MCP tools for actions (execute_sql, create_or_update_pipeline, etc.) and load specific skills for workflows (databricks-python-sdk, databricks-spark-declarative-pipelines, etc.). Use this skill when you need reference documentation.

How to Use

Fetch the llms.txt documentation index:

URL: https://docs.databricks.com/llms.txt

Use WebFetch to retrieve this index, then:

1. Search for relevant sections/links
2. Fetch specific documentation pages for detailed guidance
3. Apply what you learn using the appropriate MCP tools

Documentation Structure

The llms.txt file is organized by category:

• Overview & Getting Started - Basic concepts and tutorials
• Data Engineering - Lakeflow, Spark, Delta Lake, pipelines
• SQL & Analytics - Warehouses, queries, dashboards
• AI/ML - MLflow, model serving, GenAI
• Governance - Unity Catalog, permissions, security
• Developer Tools - SDKs, CLI, APIs, Terraform

Example: Complementing Other Skills

Scenario: User wants to create a Delta Live Tables pipeline

1. Load databricks-spark-declarative-pipelines skill for workflow patterns
2. Use this skill to fetch docs if you need clarification on specific DLT features
3. Use create_or_update_pipeline MCP tool to actually create the pipeline

Scenario: User asks about an unfamiliar Databricks feature

1. Fetch llms.txt to find relevant documentation
2. Read the specific docs to understand the feature
3. Determine which skill/tools apply, then use them

Related Skills

• databricks-python-sdk - SDK patterns for programmatic Databricks access
• databricks-spark-declarative-pipelines - DLT / Lakeflow pipeline workflows
• databricks-unity-catalog - Governance and catalog management
• databricks-model-serving - Serving endpoints and model deployment
• databricks-mlflow-evaluation - MLflow 3 GenAI evaluation workflows