About dbt Model Context Protocol (MCP)
As AI becomes more deeply integrated into data workflows, dbt users need a seamless way to access and integrate dbt's structured metadata and execution context effectively. This page provides an overview of dbt's MCP Server, which exposes this context, supporting use cases such as conversational access to data, agent-driven automation of dbt workflows, and AI-assisted development.
The dbt Model Context Protocol (MCP) server provides a standardized framework that enables users to seamlessly integrate AI applications with dbt-managed data assets regardless of the underlying data platforms. This ensures consistent, governed access to models, metrics, lineage, and freshness across various AI tools.
The MCP server provides access to the dbt CLI, API, the Discovery API, and Semantic Layer. It provides access to private APIs, text-to-SQL, and SQL execution.
For more information on MCP, have a look at Get started with the Model Context Protocol (MCP).
Server access
You can use the dbt MCP server in two ways: locally or remotely. Choose the setup that best fits your workflow:
Local MCP server
The local MCP server provides the best experience for development workflows, like authoring dbt models, tests, and documentation.
The local MCP server runs on your machine and requires installing uvx (which installs dbt-mcp locally). This option provides:
- Full access to dbt CLI commands (
dbt run,dbt build,dbt test, and more) - Support for dbt Core, dbt CLI, and dbt Fusion engine
- Ability to work with local dbt projects without requiring a dbt platform account
- Optional integration with dbt platform APIs for metadata discovery and Semantic Layer access
Remote MCP server
The remote MCP server from dbt offers data consumption use cases without local setup.
The remote MCP server connects to the dbt platform via HTTP and requires no local installation. This option is useful when:
- You either don’t want to install, or are restricted from installing, additional software on your system.
- Your use case is primarily consumption-based (for example, querying metrics, exploring metadata, viewing lineage).
Only text_to_sql consumes dbt Copilot credits. Other MCP tools do not.
When your account runs out of Copilot credits, the remote MCP server blocks all tools that run through it, even tools invoked from a local MCP server and proxied to remote MCP (like SQL and remote Fusion tools).
If you reach your dbt Copilot usage limit, all tools will be blocked until your Copilot credits reset. If you need help, please reach out to your account manager.
Available tools
The following tool list is available for your MCP server and is auto-fetched from the dbt MCP server README on GitHub when the docs are built, so it stays in sync with each release.
SQL
Tools for executing and generating SQL on dbt Platform infrastructure.
execute_sql: Executes SQL on dbt Platform infrastructure with Semantic Layer support.text_to_sql: Generates SQL from natural language using project context.
Semantic Layer
To learn more about the dbt Semantic Layer, click here.
get_dimensions: Gets dimensions for specified metrics.get_entities: Gets entities for specified metrics.get_metrics_compiled_sql: Returns compiled SQL for metrics without executing the query.list_metrics: Retrieves all defined metrics.list_saved_queries: Retrieves all saved queries.query_metrics: Executes metric queries with filtering and grouping options.
Discovery
To learn more about the dbt Discovery API, click here.
get_all_macros: Retrieves macros; option to filter by package or return package names only.get_all_models: Retrieves name and description of all models.get_all_sources: Gets all sources with freshness status; option to filter by source name.get_exposure_details: Gets exposure details including owner, parents, and freshness status.get_exposures: Gets all exposures (downstream dashboards, apps, or analyses).get_lineage: Gets full lineage graph (ancestors and descendants) with type and depth filtering.get_macro_details: Gets details for a specific macro.get_mart_models: Retrieves all mart models.get_model_children: Gets downstream dependents of a model.get_model_details: Gets model details including compiled SQL, columns, and schema.get_model_health: Gets health signals: run status, test results, and upstream source freshness.get_model_parents: Gets upstream dependencies of a model.get_model_performance: Gets execution history for a model; option to include test results.get_related_models: Finds similar models using semantic search.get_seed_details: Gets details for a specific seed.get_semantic_model_details: Gets details for a specific semantic model.get_snapshot_details: Gets details for a specific snapshot.get_source_details: Gets source details including columns and freshness.get_test_details: Gets details for a specific test.search: [Alpha] Searches for resources across the dbt project (not generally available).
dbt CLI
Allowing your client to utilize dbt commands through the MCP tooling could modify your data models, sources, and warehouse objects. Proceed only if you trust the client and understand the potential impact.
build: Executes models, tests, snapshots, and seeds in DAG order.compile: Generates executable SQL from models/tests/analyses; useful for validating Jinja logic.docs: Generates documentation for the dbt project.get_lineage_dev: Retrieves lineage from local manifest.json with type and depth filtering.get_node_details_dev: Retrieves node details from local manifest.json (models, seeds, snapshots, sources).list: Lists resources in the dbt project by type with selector support.parse: Parses and validates project files for syntax correctness.run: Executes models to materialize them in the database.show: Executes SQL against the database and returns results.test: Runs tests to validate data and model integrity.
Admin API
To learn more about the dbt Administrative API, click here.
cancel_job_run: Cancels a running job.get_job_details: Gets job configuration including triggers, schedule, and dbt commands.get_job_run_artifact: Downloads a specific artifact file from a job run.get_job_run_details: Gets run details including status, timing, steps, and artifacts.get_job_run_error: Gets error and/or warning details for a job run; option to include or show warnings only.get_project_details: Gets project information for a specific dbt project.list_job_run_artifacts: Lists available artifacts from a job run.list_jobs: Lists jobs in a dbt Platform account; option to filter by project or environment.list_jobs_runs: Lists job runs; option to filter by job, status, or order by field.retry_job_run: Retries a failed job run.trigger_job_run: Triggers a job run; option to override git branch, schema, or other settings.
dbt Codegen
These tools help automate boilerplate code generation for dbt project files.
generate_model_yaml: Generates model YAML with columns; option to inherit upstream descriptions.generate_source: Generates source YAML by introspecting database schemas; option to include columns.generate_staging_model: Generates staging model SQL from a source table.
dbt LSP
A set of tools that leverage the Fusion engine for advanced SQL compilation and column-level lineage analysis.
fusion.compile_sql: Compiles SQL in project context via dbt Platform.fusion.get_column_lineage: Traces column-level lineage via dbt Platform.get_column_lineage: Traces column-level lineage locally (requires dbt-lsp via dbt Labs VSCE).
Product Docs
Tools for searching and fetching content from the official dbt documentation at docs.getdbt.com.
get_product_doc_pages: Fetches the full Markdown content of one or more docs.getdbt.com pages by path or URL.search_product_docs: Searches docs.getdbt.com for pages matching a query; returns titles, URLs, and descriptions ranked by relevance. Use get_product_doc_pages to fetch full content.
MCP Server Metadata
These tools provide information about the MCP server itself.
get_mcp_server_branch: Returns the current git branch of the running dbt MCP server.get_mcp_server_version: Returns the current version of the dbt MCP server.
Supported tools by MCP server type
The dbt MCP server has access to many parts of the dbt experience related to development, deployment, and discovery. Here are the categories of tools supported based on what form of the MCP server you connect to as well as detailed information on exact commands or queries available to the LLM.
Note that access to the dbt APIs is limited depending on your plan type.
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MCP integrations
The dbt MCP server integrates with any MCP client that supports token authentication and tool use capabilities.
We have also created integration guides for the following clients:
Resources
- For more information, refer to our blog on Introducing the dbt MCP Server.
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