Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Schema
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| dbt_run | 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. |
| dbt_test | 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. |
| dbt_ls | 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. |
| dbt_compile | 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. |
| dbt_debug | 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. |
| dbt_deps | 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. |
| dbt_seed | 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. |
| dbt_show | 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. |
| dbt_build | 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. |