# mattijsdp/dbt-docs-mcp [Health: Active]

**Category:** 📊 Data Platforms  
**Repository:** https://github.com/mattijsdp/dbt-docs-mcp  
**GitHub Stars:** 23  
**Views:** 2  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/mattijsdp-dbt-docs-mcp

## Description
MCP server for dbt-core (OSS) users as the official dbt MCP only supports dbt Cloud. Supports project metadata, model and column-level lineage and dbt documentation.

## Claude Desktop Quick Installation
No confirmed install details yet, so no config is published. Follow the setup steps in the repository above.

## Documentation

## What mattijsdp/dbt-docs-mcp MCP server does

The mattijsdp/dbt-docs-mcp MCP server exposes dbt project metadata through the Model Context Protocol. It reads dbt Docs artifacts, including `manifest.json` and `catalog.json`, and presents graph and node information as tools that an MCP-compatible client can call.

It is intended for dbt Core projects. The available metadata covers models, sources, tests, node attributes, compiled SQL, dependencies, and column relationships. It does not execute SQL or provide direct database metadata access; those are listed as possible future extensions rather than current capabilities.

## How it works

The server uses the dbt manifest and catalog as input data. Agents can search node names, column names, or compiled SQL to locate relevant parts of a project. After finding a node, they can request its attributes or inspect its direct predecessors and successors in the dbt graph.

Column-level lineage uses additional generated files. The repository includes `scripts/create_manifest_cl.py`, which combines a dbt manifest and catalog to produce a schema mapping and a column-lineage manifest. These outputs support tracing the upstream sources of a model column and the downstream models or columns that depend on it.

## Setup and configuration

The mattijsdp/dbt-docs-mcp MCP server requires Python and `uv`. Clone the repository, enter its directory, and install the project environment with `uv sync` when preparing column-level lineage. Run the supplied preprocessing script with paths for the manifest, catalog, schema mapping output, and column-lineage output.

An MCP client configuration starts the server with `uv run`, the listed Python dependencies, and the `mcp run` command pointing to `src/mcp_server.py`. Configuration passes paths through `MANIFEST_PATH`, `SCHEMA_MAPPING_PATH`, and `MANIFEST_CL_PATH`. The example is suitable for clients that support an `mcp.json` configuration, including Claude Desktop and Cursor.

Column-lineage generation depends on project size and may take hours for a large dbt project. The basic metadata and graph features still depend on having the relevant dbt artifact files available.

## Tools and capabilities

Available operations include:

- Searching node names with `search_dbt_node_names`.
- Finding nodes by column name with `search_dbt_column_names`.
- Searching compiled node SQL with `search_dbt_sql_code`.
- Retrieving node details with `get_dbt_node_attributes`.
- Listing direct upstream dependencies with `get_dbt_predecessors`.
- Listing direct downstream dependents with `get_dbt_successors`.
- Tracing column ancestors with `get_column_ancestors`.
- Tracing column descendants with `get_column_descendants`.

## Limitations and notes

The mattijsdp/dbt-docs-mcp MCP server focuses on reading dbt metadata and lineage artifacts. Column-level lineage requires the separate preprocessing step and its generated files, and processing time can be substantial for larger projects.

_Full upstream README: https://allmcps.com/mcp/mattijsdp-dbt-docs-mcp/readme_