dbt logo

dbt

Organization
dbt-labs

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

Publisherdbt-labs
Repositorydbt-mcp
LanguagePython
Forks
117
Stars
560
Available tools
0
Transport typestdio
Categories
Analytics DataDeveloper Tools
LicenseApache-2.0
Links
GitHub
  • Connect tools to AI workflows

    dbt exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    560 stars and 117 forks from the linked repository.

dbt MCP Server

OpenSSF Best Practices

This MCP (Model Context Protocol) server provides various tools to interact with dbt. You can use this MCP server to provide AI agents with context of your project in dbt Core, dbt Fusion, and dbt Platform.

Read our documentation here to learn more. This blog post provides more details for what is possible with the dbt MCP server.

Experimental MCP Bundle

We publish an experimental Model Context Protocol Bundle (dbt-mcp.mcpb) with each release so that MCPB-aware clients can import this server without additional setup. Download the bundle from the latest release assets and follow Anthropic's mcpb CLI docs to install or inspect it.

Feedback

If you have comments or questions, create a GitHub Issue or join us in the community Slack in the #tools-dbt-mcp channel.

Architecture

The dbt MCP server architecture allows for your agent to connect to a variety of tools.

architecture diagram of the dbt MCP server

Tools

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.
  • clone: Clones selected nodes from the specified state to the target schema(s).
  • 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_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.
  • 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.
  • list_projects: Lists all projects in the dbt Platform account.
  • 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.

Examples

Commonly, you will connect the dbt MCP server to an agent product like Claude or Cursor. However, if you are interested in creating your own agent, check out the examples directory for how to get started.

Dependencies

Dependencies are pinned to specific versions and are not updated automatically. Only security-related dependency updates are submitted via automated pull requests.

Contributing

Read CONTRIBUTING.md for instructions on how to get involved!

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "dbt-mcp": {
      "command": "uvx",
      "args": [
        "--env-file",
        "<path-to-.env-file>",
        "dbt-mcp"
      ]
    }
  }
}

Use dbt MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once dbt is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add dbt as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the dbt MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "dbt": {
      "command": "npx",
      "args": [
        "-y",
        "dbt-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the dbt MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the dbt MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use dbt to help me with this task?
dbt
Sure. I read it.
Here is what I found using dbt.

Frequently asked questions

What is the dbt MCP server used for?

dbt is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use dbt MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use dbt with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use dbt MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With dbt connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect dbt MCP to TypingMind?

dbt runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does dbt MCP provide in TypingMind?

dbt exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use dbt MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If dbt requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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