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scaffold_script

Gathers prior code, project dataset paths, and cleaning steps to enable writing a new script consistent with the user's existing conventions.

Instructions

Assemble the raw material to write a NEW script from previous work.

Pulls the most relevant prior code, the project's dataset variables/paths,
and the cleaning steps — so you can write a new script in the user's own
conventions (same names, paths, packages). Call this, then write the script.

Args:
    goal: What the new script should do.
    project_id: The project to scaffold for (prioritised, then cross-project).
    language: Target language (r, python, …).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYes
project_idNo
languageNor

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description must convey behavioral traits. It describes that the tool pulls data and is non-destructive, but does not explicitly state side effects, authentication needs, or performance considerations. It provides moderate behavioral context but not full transparency.

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 concise, front-loading the purpose in the first sentence, followed by a bullet list of parameters. Every sentence adds value without redundancy. Ideal structure for quick comprehension.

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?

Given the tool's simplicity (3 params, output schema present), the description covers purpose, usage flow, and parameters adequately. It does not mention prerequisites or failure cases, but for a scaffolding tool that is relatively straightforward, this is sufficient.

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% description coverage, so the description's Args section adds meaningful semantics for each parameter: goal (what the script should do), project_id (project priority), language (target language with examples). This compensates for the schema's lack of descriptions, though some details like format or allowed values remain implicit.

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: assemble raw material from previous work to write a new script. It specifies what it pulls (prior code, dataset variables/paths, cleaning steps) and the outcome (write a new script in user's conventions). However, it does not explicitly differentiate from siblings, though no direct alternative exists.

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

Usage Guidelines3/5

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

The description provides context by saying 'Call this, then write the script', implying it is a preparatory step. But it lacks explicit when-to-use or when-not-to-use guidance, and does not mention any alternatives. The context is clear but not comprehensive.

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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