DBT Core MCP Server

An MCP (Model Context Protocol) server for interacting with DBT (Data Build Tool) projects.

Overview

This server provides tools to interact with DBT projects via the Model Context Protocol, enabling AI assistants to:

• Query DBT project metadata

• Inspect models, sources, and tests

• View compiled SQL

• Run DBT commands

• Access DBT documentation and lineage

Installation

From PyPI (when published)

The easiest way to use this MCP server is with uvx (no installation needed):

Or install it permanently:

Usage

Running the Server

The server automatically detects DBT projects from workspace roots provided by VS Code.

Configuration for VS Code

Add to your VS Code MCP settings:

Or if you prefer pipx:

For the impatient (bleeding edge from GitHub)

If you want to always run the latest code directly from GitHub:

Or with pipx:

Requirements

• DBT Core: Version 1.9.0 or higher

• Python: 3.9 or higher

• Supported Adapters: Any DBT adapter (dbt-duckdb, dbt-postgres, dbt-snowflake, etc.)

Limitations

• Python models: Not currently supported. Only SQL-based DBT models are supported at this time.

• DBT Version: Requires dbt-core 1.9.0 or higher

Features

✅ Implemented:

• Get DBT project information (version, adapter, counts)

• List all models with metadata

• List all sources

• Automatic environment detection (uv, poetry, pipenv, venv, conda)

• Bridge runner for executing DBT in user's environment

🚧 Planned:

• Get model information and metadata (enhanced)

• View compiled SQL

• Run specific models

• Test models

• View model lineage

• Access DBT documentation

• Execute custom DBT commands

Available Tools

get_project_info

Returns metadata about the DBT project including:

• Project name

• DBT version

• Adapter type (e.g., duckdb, postgres, snowflake)

• Model and source counts

list_models

Lists all models in the project with:

• Name and unique ID

• Schema and database

• Materialization type (table, view, incremental, etc.)

• Tags

• Dependencies

• File path

list_sources

Lists all sources in the project with:

• Source and table names

• Schema and database

• Description and tags

How It Works

This server uses a "bridge runner" approach to execute DBT in your project's Python environment:

1. Environment Detection: Automatically detects your Python environment (uv, poetry, pipenv, venv, conda)

2. Subprocess Bridge: Executes DBT commands using inline Python scripts in your environment

3. Manifest Parsing: Reads target/manifest.json for model and source metadata

4. No Version Conflicts: Uses your exact dbt-core version and adapter without conflicts

Contributing

Want to help improve this server? Check out CONTRIBUTING.md for development setup and guidelines.

License

MIT License - see LICENSE file for details.

Author

Niclas Olofsson - GitHub