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dbt CLI MCP Server

by MammothGrowth

Overview Schema Related Servers Score Discussions

Python

Hybrid

dbt_seed

Load CSV files as seed data to create reference tables, test datasets, or static data for database models.

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

TableJSON Schema

Name	Required	Description	Default
`selector`	No	Named selector to use
`exclude`	No	Seeds to exclude
`project_dir`	No	ABSOLUTE PATH to the directory containing the dbt project (e.g. '/Users/username/projects/dbt_project' not '.')	.
`profiles_dir`	No	Directory containing the profiles.yml file (defaults to project_dir if not specified)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Implementation Reference

src/tools.py:311-350 (handler)

The primary MCP tool handler for 'dbt_seed'. Defines input schema via Pydantic Fields and implements the logic to execute 'dbt seed' command with optional selector and exclude parameters.

@mcp.tool()
async def dbt_seed(
    selector: Optional[str] = Field(
        default=None,
        description="Named selector to use"
    ),
    exclude: Optional[str] = Field(
        default=None,
        description="Seeds to exclude"
    ),
    project_dir: str = Field(
        default=".",
        description="ABSOLUTE PATH to the directory containing the dbt project (e.g. '/Users/username/projects/dbt_project' not '.')"
    ),
    profiles_dir: Optional[str] = Field(
        default=None,
        description="Directory containing the profiles.yml file (defaults to project_dir if not specified)"
    )
) -> str:
    """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)
    """
    command = ["seed"]

    # The --no-print flag is not supported by dbt Cloud CLI
    # We'll rely on proper parsing to handle any print macros

    if selector:
        command.extend(["--selector", selector])

    if exclude:
        command.extend(["--exclude", exclude])

    result = await execute_dbt_command(command, project_dir, profiles_dir)

    # Use the centralized result processor
    return await process_command_result(result, command_name="seed")

src/server.py:89-89 (registration)
Registers all MCP tools, including 'dbt_seed', by calling register_tools on the FastMCP server instance.
```
register_tools(mcp)
```
src/tools.py:25-25 (registration)
The register_tools function that defines and registers the 'dbt_seed' tool using @mcp.tool() decorator.
```
def register_tools(mcp: FastMCP) -> None:
```

Tool Definition Quality

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by explaining the tool's purpose and use cases. It adds valuable context about the output format ('Returns: Output from the dbt seed command as text (this command does not support JSON output format)'), which is crucial behavioral information not in the input schema. However, it doesn't mention potential side effects like database writes or performance implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose first, then usage guidelines, and finally output format. Every sentence adds value, though the output format section could be slightly more integrated rather than a separate 'Returns:' block.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Good completeness for a data loading tool with no annotations but an output schema. The description covers purpose, usage context, and output behavior. With 4 parameters fully documented in the schema and an output schema present, the main gap is lack of explicit warnings about database writes or permissions needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing good documentation for all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without compensating with extra semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Load CSV files as seed data') and resource ('database'), distinguishing it from siblings by focusing on initial data loading from CSV files rather than building, compiling, testing, or other dbt operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool ('when it needs to load initial data from CSV files into the database') and provides concrete use cases ('creating reference tables, test datasets, or any static data that models will depend on'), giving clear context for selection among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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