DBT Docs MCP

The dbt‑docs‑mcp server turns the rich metadata generated by a dbt project into a conversational, queryable interface that can be leveraged by AI assistants. Instead of manually navigating the and files, developers can ask a language model to locate a specific model, retrieve its lineage, or trace the origins of a column—all through natural language. This eliminates the need to parse large JSON files or write custom scripts, speeding up data‑engineering workflows and reducing cognitive load for analysts.

At its core, the server exposes a set of intuitive tools that mirror common data‑engineering tasks. Developers can search for nodes by name, column, or even within the compiled SQL code, allowing quick discovery of models or tests that match a pattern. Once a node is identified, the server can return its full attribute set—including description, tags, and configuration—giving a comprehensive view of that artifact without leaving the AI interface. Lineage is handled at both the node and column level: predecessors and successors of a model are retrievable, as well as the full ancestry or descendant chain for any column. This granular lineage support is crucial when troubleshooting downstream issues, auditing data quality, or explaining model dependencies to stakeholders.

The server’s design is tightly aligned with MCP best practices. It uses environment variables to locate the dbt artifacts, enabling seamless integration into CI/CD pipelines or local development setups. The tooling is lightweight yet powerful: it leverages for graph traversal, for fuzzy searching, and dbt’s own Python libraries to parse the metadata. By exposing these capabilities as MCP tools, any AI client that supports MCP—such as Claude Desktop or Cursor—can invoke them with a single prompt, making the experience feel native to the user.

Real‑world scenarios that benefit from this server include data‑catalog discovery, impact analysis before deploying a new model, or automated documentation generation. For instance, an analyst can ask the assistant to “show me all downstream models of that reference column ,” and the assistant will return a concise graph and relevant node details. Similarly, data‑governance teams can query for all tests that involve a particular source table, ensuring compliance checks are automated and auditable.

Unique advantages of the dbt‑docs‑mcp include its column‑level lineage capability—most other MCP servers only expose node‑level relationships—and its optional script to generate a column lineage file from the dbt manifest and catalog. This pre‑processing step, while potentially time‑consuming for large projects, yields a ready‑to‑query structure that dramatically speeds up subsequent queries. By coupling this with the MCP interface, developers gain a powerful, AI‑driven way to explore and understand their dbt data models without writing any code.