"dbt" matching MCP servers:

A MCP (Model Context Protocol) server for interacting with dbt.

AI-driven MCP server that audits, profiles, detects schema drift, and auto-generates documentation for dbt projects, enabling natural language interaction with your dbt project's health.

Enables AI agents to inspect, query, test, and safely fix dbt projects and BigQuery warehouses under policy-enforced controls, with read-only direct queries and dbt-only allowlisted writes.

An MCP server that exposes a dbt project's run state as tools, enabling AI assistants to answer questions like 'is the warehouse healthy?' or 'what broke and why?' via plain English, using read-only access to dbt artifacts and warehouse data.

MCP (model context protocol) server for interacting with dbt Docs

Enables AI assistants to interact with DBT (Data Build Tool) projects, allowing them to query project metadata, inspect models and sources, view compiled SQL, and run DBT commands.

A read-only MCP server that exposes dbt project artifacts and data quality result tables (BigQuery/Postgres) to LLM clients, enabling deep introspection, run-history analysis, source freshness, test coverage, and lineage walks.

Enables the analysis of DBT manifests with automatic schema version detection and lineage tracking, allowing users to query model dependencies, access compiled code, and get detailed model information.

A server that enables querying the dbt Semantic Layer through natural language conversations with Claude Desktop and other AI assistants, allowing users to discover metrics, create queries, analyze data, and visualize results.

Read-only-by-default MCP server for Datanika, an open-source dlt + dbt-core data pipeline platform: browse connections, introspect and preview source tables, compile and run dbt transformations, and monitor pipeline runs. 25 tools (17 read-only, plus 8 write tools gated behind --allow-write); source lives in the datanika-mcp/ subdirectory of the repo.

Enables AI agents to query live schema, lineage, and query-context across data warehouses, dbt projects, orchestration systems, and BI tools via MCP tools.

Enables language models to run data-quality checks and profiling on local files, using dbt-style assertions like not_null, unique, relationships, and accepted_values.

Enables employees to ask natural-language questions about a client's dbt semantic layer through the chatbot they already use, asking for clarification when needed, refusing when data can't answer, and logging every interaction so the semantic layer improves over time.

MCP server providing read-only Snowflake metadata tools (schemas, tables, queries, lineage) for agentic data pipeline generation, enabling natural-language-to-pipeline workflows with dbt, Airflow, and Great Expectations.

Enables Claude to interact with a local DuckDB lakehouse by inspecting Gold table schemas, running read-only SQL queries, and checking dbt pipeline health.

Enables AI assistants to search NHTSA recall documents and query governed dbt analytics via validated, read-only text-to-SQL, returning citable evidence and data freshness without granting database or file access.

Provides specialized tools for data engineering tasks like SQL formatting, dbt model generation, and Snowflake table creation. It enables users to analyze CSV data, validate pipeline configurations, and summarize ETL lineage through natural language.

Exposes a governed semantic layer built on dbt Core and DuckDB, enabling AI agents to query predefined metric definitions for a P&C insurance dataset. Prevents metric hallucination by restricting agents to governed tools and read-only data access.

An MCP server that gives AI agents access to configured databases (PostgreSQL, MySQL, Redshift, SQL Server) with SSH/AWS SSM tunnels, pluggable secret providers, and strict per-instance isolation.

Enables AI agents to safely interact with a SQLite shop database through schema discovery, read-only SQL queries, and pre-built analytics reports like top customers, top products, and revenue summaries.