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dbt

dbt

by dbt Labs
2 views
v1.22.1
Apache License Version 2.
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Databases & Data
datadbtanalyticssemantic-layer

Summary

dbt Labs' official MCP server — explore project metadata, run dbt commands, and query the Semantic Layer.

Features

  • Explore models, sources, and lineage in a dbt project
  • Run dbt commands and read the results
  • Query the Semantic Layer for governed metrics

Installation

Set up this MCP server in your favorite AI agent — copy a ready-made configuration below.

Any MCP-compatible agent

Most agents (Claude, Cursor, Windsurf, VS Code, and more) read a standard mcpServers configuration.

  1. Open your agent's MCP configuration file.
  2. Merge the snippet below into it.
  3. Restart the agent — the "dbt" tools will be available.
{
  "mcpServers": {
    "dbt": {
      "command": "uvx",
      "args": [
        "dbt-mcp"
      ]
    }
  }
}

Description

dbt Labs' official MCP server connects an assistant to a dbt project and, if you use dbt Cloud, to its metadata and Semantic Layer.

Three areas of capability
  • Project discovery. List models, sources, and tests; read model definitions; and trace lineage upstream and downstream. This is the fastest way to answer "what feeds this table and what breaks if I change it".
  • Command execution. Run dbt build, run, test, and compile and read the output, so an assistant can iterate on a model until its tests pass.
  • Semantic Layer queries. Ask for a metric by name and get governed, consistent numbers rather than a hand-written SQL approximation that quietly disagrees with the dashboard.
Why the Semantic Layer part matters

The recurring problem with AI querying a warehouse is that it invents its own definition of a metric. Routing through the Semantic Layer means "revenue" means what your organisation has agreed it means.

Installed with uvx dbt-mcp, configured with your project directory and, optionally, dbt Cloud credentials.

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