@dbt-labs

configuring-dbt-mcp-server guides users through server setup (local vs remote), client selection, and credential configuration with working json templates. covers four mcp clients and two auth strategies.

using-dbt-for-analytics-engineering covers model building, sql transformations, testing, and validation through dbt's abstraction layer. applies software engineering discipline (dry, modularity, testing) to data pipeline work with emphasis on iterative validation and cost awareness.

fetching-dbt-docs provides a two-tier lookup mechanism (index then full search) for retrieving dbt documentation in markdown format, with explicit guidance on url patterns and a search script fallback.

troubleshooting-dbt-job-errors guides systematic diagnosis of dbt cloud job failures through log analysis, api querying, and data investigation. covers when to use and the principle of investigating root causes before modifying tests.

adding-dbt-unit-test provides reference links and warehouse-specific guidance but lacks actionable steps, decision logic, or concrete procedures for writing dbt unit tests.

building-dbt-semantic-layer documents the conceptual taxonomy of semantic layer components (models, entities, dimensions, metrics) but lacks actionable procedure for constructing them. mostly reference pointers without guidance.

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about…

Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles,…