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Metis · Software Engineer — Scaffold Script

scaffold_script

Assembles relevant prior code, dataset paths, and cleaning steps to scaffold writing a new script in the user's 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
languageNor
project_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description details that the tool pulls prior code, variables, paths, and cleaning steps, and does not indicate any destructive actions. Though no annotations are provided, it conveys a read‑only assembly behavior. It could be more explicit about non‑mutability, but overall it is transparent.

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 with two short paragraphs and a bullet‑style argument list. It front‑loads the core purpose and provides all essential information without extraneous text. Every sentence is purposeful.

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

Completeness5/5

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

Given the tool's straightforward purpose (scaffolding a new script), the description adequately covers what it does, what inputs are needed, and how to use it in sequence. An output schema exists, so full detail on return values is not required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema lacks descriptions (0% coverage), but the tool's description meaningfully defines all three parameters: goal, project_id, and language, explaining their purpose and defaults (e.g., language default 'r'). This adds significant value beyond the bare 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?

The description clearly states it 'Assemble the raw material to write a NEW script from previous work,' specifying the verb and resource. It distinguishes from sibling tools like 'analyze_script' by focusing on creating new scripts rather than analyzing existing ones.

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 advises 'Call this, then write the script,' providing a clear sequence of use. It also explains the scope of project_id (prioritised then cross-project). However, it does not explicitly state when not to use this tool or mention alternatives, which would be beneficial.

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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