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, manage_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:
- Search for relevant sections/links
- Fetch specific documentation pages for detailed guidance
- 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
- Load
databricks-spark-declarative-pipelinesskill for workflow patterns - Use this skill to fetch docs if you need clarification on specific DLT features
- Use
manage_pipeline(action="create_or_update")MCP tool to actually create the pipeline
Scenario: User asks about an unfamiliar Databricks feature
- Fetch llms.txt to find relevant documentation
- Read the specific docs to understand the feature
- 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