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dbt-mcp

dbt-mcp

Official
by dbt-labs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

test

Run data tests on models, sources, snapshots, and seeds, and execute unit tests on SQL models to ensure data quality and accuracy in your dbt projects.

Instructions

dbt test runs data tests defined on models, sources, snapshots, and seeds and unit tests defined on SQL models.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "test"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that the tool 'runs' tests, implying a read-only or execution operation, but doesn't clarify if it's destructive (e.g., modifies data), requires specific permissions, has rate limits, or what the output entails (e.g., test results, logs). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('runs data tests') and specifies the scope clearly. Every part of the sentence contributes to understanding the purpose, making it highly concise and well-structured.

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

Completeness3/5

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

Given the tool has 0 parameters, no annotations, and no output schema, the description is minimally adequate. It explains what the tool does (runs tests on dbt objects), but doesn't cover behavioral aspects like output format, error handling, or integration with siblings. For a simple execution tool, it meets the basic need but lacks depth for full contextual understanding.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate since there are none. It adds no semantic details beyond the schema, but with zero parameters, the baseline is high as there's nothing to compensate for, and the description doesn't mislead about parameters.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'runs data tests defined on models, sources, snapshots, and seeds and unit tests defined on SQL models.' It specifies the verb ('runs') and the resources (tests on various dbt objects), making it clear what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'run' or 'build', which might also involve execution, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for testing (e.g., after building models), or comparisons to siblings like 'run' (which might execute models) or 'list' (which might list tests). Without such guidance, the agent lacks direction on appropriate usage scenarios.

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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