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build_wheel

Builds Python wheels for Databricks Lakeflow jobs using uv build, enabling AI agents to package and deploy data experiments to clusters.

Instructions

Builds the Python wheel using 'uv build --wheel'.

Args:
    target: The path to the directory containing pyproject.toml.

Returns:
    The path to the generated wheel file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 mentions the command executed ('uv build --wheel') and the return value, but doesn't disclose critical behavioral traits like whether this is a read-only operation, what happens on failure, whether it modifies the filesystem, or any side effects. The description is insufficient for a mutation tool with zero annotation coverage.

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 perfectly structured and concise with zero wasted words. It uses a clear main sentence followed by organized Args and Returns sections. Every sentence earns its place by providing essential information about the tool's purpose, parameters, and return value.

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 that this is a build tool with no annotations but with an output schema (which handles return value documentation), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks important behavioral context about what the tool actually does beyond the command execution. For a tool that likely modifies the filesystem, more disclosure would be beneficial.

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 description adds meaningful context about the single parameter beyond what the schema provides. While schema description coverage is 0%, the description clarifies that 'target' is 'The path to the directory containing pyproject.toml', which provides essential semantic understanding not present in the schema's minimal title 'Target'. This compensates well for the schema's lack of documentation.

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 ('Builds the Python wheel') and the implementation method ('using "uv build --wheel"'), distinguishing it from sibling tools like upload_wheel. It provides a precise 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 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 like upload_wheel or trigger_run. It mentions the target parameter but doesn't explain prerequisites (e.g., needing pyproject.toml present) or when this operation is appropriate versus other build methods.

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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