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compile

Generate executable SQL from dbt model, test, and analysis files. Validate complex Jinja logic and macro usage by inspecting compiled output directly.

Instructions

dbt compile generates executable SQL from source model, test, and analysis files.

The compile command is useful for visually inspecting the compiled output of model files. This is useful for validating complex jinja logic or macro usage.

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 but only partially discloses behavioral traits. It mentions the tool is 'useful for visually inspecting' and 'validating,' implying it's a read-only/safe operation, but doesn't clarify if it modifies any state, requires specific permissions, has side effects, or details about output format (though no output schema exists). More behavioral context is needed given the lack of annotations.

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 efficiently structured in two sentences: the first states the core purpose, and the second provides usage context. Every sentence adds value without redundancy, making it appropriately sized and front-loaded for quick understanding.

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's complexity (compiling SQL from multiple file types) and lack of annotations/output schema, the description is moderately complete. It explains the purpose and usage but lacks details on behavioral aspects like side effects, error handling, or output format. For a tool with no structured data support, more context would improve completeness.

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, so no parameter documentation is needed. The description appropriately focuses on tool behavior rather than parameters, earning a high baseline score. It doesn't add parameter details beyond the schema, which is fine since there are none to document.

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 ('generates executable SQL') and resources involved ('from source model, test, and analysis files'), distinguishing it from sibling tools like 'run' or 'test' that likely execute rather than compile. It provides a concrete verb+resource combination that leaves no ambiguity about the tool's function.

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

Usage Guidelines4/5

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

The description explicitly states when to use this tool: 'for visually inspecting the compiled output' and 'for validating complex jinja logic or macro usage.' However, it doesn't specify when NOT to use it or mention alternatives among the many sibling tools (like 'run' for execution or 'parse' for parsing), leaving some guidance gaps.

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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