dbt

SQL-first transformation workflow that enables analytics engineers to transform data in the warehouse.

MCPCLI8 Skills

About dbt

SQL-first transformation workflow that enables analytics engineers to transform data in the warehouse. Explore how dbt integrates with the agentic data stack ecosystem and supports autonomous data operations.

Key Features

  • SQL-first transformation framework — write SELECT statements, dbt handles DDL/DML
  • Version-controlled, testable data transformations with Git-based workflows
  • Built-in data testing framework (schema tests, custom tests, freshness checks)
  • DAG-based dependency management for model execution ordering
  • Jinja templating for dynamic SQL and reusable macros
  • Auto-generated documentation with data lineage visualization
  • Incremental model support for efficient large-dataset processing
  • Package ecosystem (dbt Hub) for reusable transformation libraries

Agent Integration

CLI — dbt

$ pip install dbt-core
CLI Documentation

External Links

dbt MCP Server (Official)

Official MCP server with 60+ tools across 8 toolset categories for AI agent integration

dbt MCP Documentation

Official docs on setting up and using the dbt MCP server with AI agents

CLI Command Reference

Complete CLI reference for all dbt commands (build, run, test, compile, docs)

Semantic Layer API Overview

JDBC, GraphQL, and Python SDK APIs for querying dbt Semantic Layer metrics

dbt Cloud APIs

REST and GraphQL APIs for dbt Cloud including admin, discovery, and semantic layer

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