Dbt MCP: MCP server, setup and security

What Dbt MCP does

According to its repository: “dbt integration: dbt Core and Cloud CLI, project metadata discovery, model information and semantic layer queries.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Dbt MCP is listed under Data platforms, a category of 39 servers in the directory, where it ranks #4 by stars.

Topics declared by the repository: data-analytics, data-engineering, dbt.

Installing and connecting Dbt MCP

No package or remote server is declared in the official MCP registry: you install it from source, following the repository README.

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: Python.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Dbt MCP. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on March 12, 2025, the dbt-labs/dbt-mcp repository has 612 stars and 142 forks, with 30 open issues. It has more stars than 89% of the servers in the directory. It received changes this month.

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.