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MammothGrowth

dbt CLI MCP Server

by MammothGrowth
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Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
dbt_runA

Run dbt models. An AI agent should use this tool when it needs to execute dbt models to transform data and build analytical tables in the data warehouse. This is essential for refreshing data or implementing new data transformations in a project.

    Returns:
        Output from the dbt run command as text (this command does not support JSON output format)
dbt_testA

Run dbt tests. An AI agent should use this tool when it needs to validate data quality and integrity by running tests defined in a dbt project. This helps ensure that data transformations meet expected business rules and constraints before being used for analysis or reporting.

    Returns:
        Output from the dbt test command as text (this command does not support JSON output format)
dbt_lsA

List dbt resources. An AI agent should use this tool when it needs to discover available models, tests, sources, and other resources within a dbt project. This helps the agent understand the project structure, identify dependencies, and select specific resources for other operations like running or testing.

    Returns:
        When output_format is 'json' (default):
          - With verbose=False (default): returns a simplified JSON with only name, resource_type, and depends_on.nodes
          - With verbose=True: returns a full JSON with all resource details
        When output_format is 'name', 'path', or 'selector', returns plain text with the respective format.
dbt_compileA

Compile dbt models. An AI agent should use this tool when it needs to generate the SQL that will be executed without actually running it against the database. This is valuable for validating SQL syntax, previewing transformations, or investigating how dbt interprets models before committing to execution.

    Returns:
        Output from the dbt compile command as text (this command does not support JSON output format)
dbt_debugA

Run dbt debug to validate the project setup. An AI agent should use this tool when it needs to troubleshoot configuration issues, check database connectivity, or verify that all project dependencies are properly installed. This is essential for diagnosing problems before attempting to run models or tests.

    Returns:
        Output from the dbt debug command as text (this command does not support JSON output format)
dbt_depsA

Install dbt package dependencies. An AI agent should use this tool when it needs to install or update external packages that the dbt project depends on. This ensures that all required modules, macros, and models from other packages are available before running the project.

    Returns:
        Output from the dbt deps command as text (this command does not support JSON output format)
dbt_seedA

Load CSV files as seed data. An AI agent should use this tool when it needs to load initial data from CSV files into the database. This is essential for creating reference tables, test datasets, or any static data that models will depend on.

    Returns:
        Output from the dbt seed command as text (this command does not support JSON output format)
dbt_showA

Preview the results of a model. An AI agent should use this tool when it needs to preview data from a specific model without materializing it. This helps inspect transformation results, debug issues, or demonstrate how data looks after processing without modifying the target database.

    Returns:
        Output from the dbt show command, defaulting to JSON format if not specified
dbt_buildA

Run build command (seeds, tests, snapshots, and models). An AI agent should use this tool when it needs to execute a comprehensive build process that runs seeds, snapshots, models, and tests in the correct order. This is ideal for complete project deployment or ensuring all components work together.

    Returns:
        Output from the dbt build command as text (this command does not support JSON output format)

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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TDQS

A4.5/5.0

Scored across 9 tools

Disambiguation5/5

Each tool corresponds to a distinct dbt CLI command with a clear, non-overlapping purpose. The descriptions explicitly differentiate their use cases, such as dbt_build for comprehensive builds versus dbt_run for model execution, preventing agent misselection.

Naming Consistency5/5

All tool names follow a consistent 'dbt_' prefix with the exact CLI command name in lowercase, such as dbt_build, dbt_compile, and dbt_debug. This uniform pattern makes the set predictable and easy to navigate for agents.

Tool Count5/5

With 9 tools, the server covers core dbt CLI operations without being overwhelming. This count aligns well with the domain, providing essential commands for project management, execution, and validation in a data transformation workflow.

Completeness5/5

The tool set offers comprehensive coverage of dbt CLI functionality, including project setup (debug, deps), resource listing (ls), execution (run, build, seed, test), and inspection (compile, show). No obvious gaps exist for typical agent workflows in this domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources