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scaffold_script

Retrieve relevant prior code, dataset variables, and cleaning steps from your projects to scaffold a new script in your own 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?

No annotations provided, so description carries full burden. It discloses that it pulls prior code, dataset variables/paths, and cleaning steps. However, it does not mention side effects, authentication needs, or whether it modifies anything. For a read-oriented scaffold tool, this is adequate but not exhaustive.

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

Conciseness4/5

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

Description is two sentences plus an Args list, front-loaded with purpose. Efficient and no fluff. Could be slightly more structured, but it earns its length.

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 3 parameters, no annotations, but an output schema exists, the description adequately explains the tool's effect and inputs. It covers what is pulled and the steps to follow. Return values are left to output schema, which is acceptable.

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?

Schema coverage is 0%, and description provides explicit meanings for all three parameters: goal ('what the new script should do'), project_id ('project to scaffold for, prioritised then cross-project'), language ('r, python, …'). This compensates well for sparse schema.

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?

Description clearly states it 'assembles raw material to write a new script', specifying verb and resource. Distinguishes from siblings by focusing on scaffolding, not writing or editing. No sibling tool offers similar functionality.

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?

States 'Call this, then write the script', indicating it's a preparatory step before script writing. Provides clear context but does not explicitly exclude alternative tools or mention when not to use. However, sibling list has no competing tool, so guidance is adequate.

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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