In April, we released the local dbt MCP (Model Context Protocol) server as an open source project to connect AI agents and LLMs with direct, governed access to trusted dbt assets. The dbt MCP server provides a universal, open standard for bridging AI systems with your structured context that keeps your agents accurate, governed, and trustworthy. Learn more in About dbt Model Context Protocol.

Since releasing the local dbt MCP server, the dbt community has been applying it in incredible ways including agentic conversational analytics, data catalog exploration, and dbt project refactoring. However, a key piece of feedback we received from AI engineers was that the local dbt MCP server isn’t easy to deploy or host for multi-tenanted workloads, making it difficult to build applications on top of the dbt MCP server.

This is why we are excited to announce a new way to integrate with dbt MCP: the remote dbt MCP server. The remote dbt MCP server doesn’t require installing dependencies or running the dbt MCP server in your infrastructure, making it easier than ever to build and run agents. It is available today in public beta for users with dbt Starter, Enterprise, or Enterprise+ plans, ready for you to start building AI-powered applications.

dbt is the standard for creating governed, trustworthy datasets on top of your structured data. MCP is showing increasing promise as the standard for providing context to LLMs to allow them to function at a high level in real world, operational scenarios.

Today, we are open sourcing an experimental version of the dbt MCP server. We expect that over the coming years, structured data is going to become heavily integrated into AI workflows and that dbt will play a key role in building and provisioning this data.