mattijsdp/dbt-docs-mcp
by Various
MCP (model context protocol) server for interacting with dbt Docs
MCP
mattijsdp/dbt-docs-mcp
Added 1 June 2026
Overview
An MCP server that enables interaction with dbt Docs through the Model Context Protocol. It allows MCP clients to query and explore dbt documentation programmatically.
Best for
Best for
Developers using dbt who want to query their documentation through MCP-compatible assistants or tools
Use cases
- Querying dbt model metadata and lineage from documentation
- Integrating dbt Docs into AI-assisted development workflows
- Automating documentation lookups via MCP-compatible tools
How to use
Tools exposed
uvgitpython
Tested with
Claude Desktop, Cursor
Example client config
{\n "mcpServers": {\n "DBT Docs MCP": {\n "command": "uv",\n "args": [\n "run",\n "--with",\n "networkx,mcp[cli],rapidfuzz,dbt-core,python-decouple,sqlglot,tqdm",\n "mcp",\n "run",\n "/Users/mattijs/repos/dbt-docs-mcp/src/mcp_server.py"\n ],\n "env": {\n "MANIFEST_PATH": "/Users/mattijs/repos/dbt-docs-mcp/inputs/manifest.json",\n "SCHEMA_MAPPING_PATH": "/Users/mattijs/repos/dbt-docs-mcp/outputs/schema_mapping.json",\n "MANIFEST_CL_PATH": "/Users/mattijs/repos/dbt-docs-mcp/outputs/manifest_column_lineage.json"\n }\n }\n }\n} Notes
An MCP server that enables interaction with dbt Docs through the Model Context Protocol. It allows MCP clients to query and explore dbt documentation programmatically.
23 stars on GitHub. Last updated 2025-08-19. Licensed MIT.
Use cases
- Querying dbt model metadata and lineage from documentation
- Integrating dbt Docs into AI-assisted development workflows
- Automating documentation lookups via MCP-compatible tools
Pros
- Leverages the standard MCP protocol for broad compatibility
- Lightweight Python implementation easy to extend
- Directly connects to existing dbt Docs output
Cons
- Small community and limited adoption (23 stars)
- Requires a running MCP client to be useful
- Documentation and examples may be sparse
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Leverages the standard MCP protocol for broad compatibility
- Lightweight Python implementation easy to extend
- Directly connects to existing dbt Docs output
Cons
- Small community and limited adoption (23 stars)
- Requires a running MCP client to be useful
- Documentation and examples may be sparse
Pairs with
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