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dbt-labs
by dbt-labs

build

Execute models, tests, snapshots, and seeds in a directed acyclic graph (DAG) order to ensure consistent and accurate data transformation workflows.

Instructions

The dbt build command will:

  • run models

  • test tests

  • snapshot snapshots

  • seed seeds

In DAG order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes the sequence of operations (run, test, snapshot, seed in DAG order) but lacks critical details: whether this is a read-only or destructive operation, permission requirements, execution time implications, error handling, or output format. For a complex multi-operation tool with zero annotation coverage, this is insufficient.

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 extremely concise and well-structured: a single introductory sentence followed by a bulleted list of four operations and a closing note about execution order. Every element adds value with zero wasted words.

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

Completeness2/5

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

Given the tool's complexity (executing four different operation types in sequence), absence of annotations, and no output schema, the description is incomplete. It explains what operations occur but not their behavioral implications, success/failure conditions, or what results to expect. For a build tool in a data transformation context, this leaves significant gaps.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description appropriately doesn't discuss parameters, maintaining focus on the tool's behavior. Baseline for 0 parameters is 4.

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 executes multiple dbt operations (run models, test tests, snapshot snapshots, seed seeds) in DAG order. It specifies the verb 'command will' and the resources affected, but doesn't explicitly differentiate from siblings like 'run' or 'test' which might perform subsets of these operations.

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?

No guidance is provided on when to use this tool versus alternatives like 'run', 'test', or 'seed'. The description lists what it does but doesn't indicate appropriate contexts, prerequisites, or exclusions compared to 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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