dbt

Executes models to materialize them in the database. Runs SQL transformations defined in dbt models

Runs tests to validate data and model integrity. Executes data quality checks and schema tests

Runs a query against the data warehouse and displays results. Useful for ad-hoc querying and validation

Gets the lineage of a model from the local development environment showing upstream and downstream dependencies

Gets details about a specific node (model, test, source, etc.) from the local development environment

Retrieves all defined metrics from the dbt Semantic Layer. Lists available business metrics for querying

Retrieves all saved queries from the dbt Semantic Layer. Lists pre-defined analytical queries

Gets dimensions associated with specified metrics from the Semantic Layer. Shows available grouping and filtering options

Gets entities associated with specified metrics from the Semantic Layer. Shows primary and foreign key relationships

Query metrics with optional grouping, ordering, filtering, and limiting. Execute analytical queries against the Semantic Layer

Returns the compiled SQL generated for specified metrics and groupings without executing the query. Useful for debugging and understanding metric logic

Gets all models in the dbt project with metadata including names, schemas, tags, and configuration

Gets all mart models (final transformed models for business use) with filtering and metadata

Gets comprehensive details for a specific model including SQL, columns, tests, documentation, and configuration

Gets the parent nodes (upstream dependencies) of a specific model showing data sources and transformations

Gets the children models (downstream dependencies) showing where model data is used

Gets health signals for a specific model including test pass rates, freshness, and execution status

Gets execution information for models including runtime, row counts, and test results

Gets all source tables with metadata and freshness information showing upstream data sources

Gets complete lineage (ancestors/descendants) for a dbt resource with depth control and type filtering (excludes macros by default)

Gets details for a specific source table including schema, columns, and freshness configuration

Gets all exposures (downstream uses of dbt models like dashboards, reports, ML models)

Gets details for a specific exposure or list of exposures including dependencies and metadata

Uses semantic search to find dbt models similar to the query even without exact string match. AI-powered model discovery

Gets details for a specific macro including SQL, arguments, and documentation

Gets details for a specific seed (CSV file loaded into database) including columns and configuration

Gets details for a specific semantic model including metrics, dimensions, and entities

Gets details for a specific snapshot (slowly changing dimension implementation)

Gets details for a specific test including SQL, configuration, and recent results

Searches for dbt resources using natural language queries. AI-powered search across models, tests, sources, and docs

List all jobs in a dbt account with filtering options by project, environment, or status

Get detailed information for a specific job including configuration, settings, schedule, and environment

Get project information for a specific dbt project including repositories, environments, and connections

Trigger a job run with optional parameter overrides like Git branch, schema, or execution parameters

List runs in an account with optional filtering by job, status, time range, or other criteria

Get comprehensive run information including execution details, steps, artifacts, timing, and debug logs

Cancel a running job to stop execution immediately. Useful for long-running or stuck jobs

Retry a failed job run to attempt execution again with same or modified parameters

List all available artifacts for a job run (manifest.json, catalog.json, run_results.json, logs, etc.)

Download specific artifact files from job runs for analysis, debugging, or integration with other tools

Retrieves error details for failed job runs to help troubleshoot errors (includes option to return warning and deprecation details)

Generate SQL from natural language requests using AI. Consumes dbt Copilot credits. Requires understanding of project context and schema

Execute SQL on dbt platform backend infrastructure with support for Semantic Layer SQL syntax. Note: using a PAT instead of service token for DBT_TOKEN is required

Creates source YAML definitions from database schemas automatically. Requires dbt-codegen package. Disabled by default (set DISABLE_DBT_CODEGEN=false to enable)

Generates documentation YAML for existing dbt models including column names, data types, and description placeholders. Requires dbt-codegen. Disabled by default

Creates staging SQL models from sources to transform raw source data into clean staging models. Requires dbt-codegen. Disabled by default

Compiles a SQL statement in the context of the current project and environment using Fusion engine. Remote MCP tool

Fusion exclusive! Get column-level lineage information across a project DAG for a specific column. Available in both local (via LSP) and remote MCP

Returns the current version of the dbt MCP server. Disabled by default (set DISABLE_MCP_SERVER_METADATA=false to enable)