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dbt CLI MCP Server

dbt_seed

Load CSV files as seed data into a database to create reference tables, test datasets, or static data for models. Use this tool to initialize essential data for dbt projects directly from CSV files.

Instructions

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)

Input Schema

NameRequiredDescriptionDefault
excludeNoSeeds to exclude
profiles_dirNoDirectory containing the profiles.yml file (defaults to project_dir if not specified)
project_dirNoABSOLUTE PATH to the directory containing the dbt project (e.g. '/Users/username/projects/dbt_project' not '.').
selectorNoNamed selector to use

Input Schema (JSON Schema)

{ "properties": { "exclude": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Seeds to exclude", "title": "Exclude" }, "profiles_dir": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Directory containing the profiles.yml file (defaults to project_dir if not specified)", "title": "Profiles Dir" }, "project_dir": { "default": ".", "description": "ABSOLUTE PATH to the directory containing the dbt project (e.g. '/Users/username/projects/dbt_project' not '.')", "title": "Project Dir", "type": "string" }, "selector": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Named selector to use", "title": "Selector" } }, "title": "dbt_seedArguments", "type": "object" }
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