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record_dataset_treatment

Record a dataset cleaning or transformation step to build a traceable lineage from raw to cleaned data, enabling reproducible results.

Instructions

Record one cleaning/transformation step for a dataset (its lineage).

Build a traceable chain raw → cleaned → analysis dataset, so any result can
be reproduced. Call once per step (recode, filter, join, derive, …).

Args:
    dataset_name: The dataset being transformed.
    description: What this step does (e.g. "drop records with missing age").
    project_id: Project this belongs to. Optional.
    step_type: recode | filter | join | derive | clean | other.
    code: The code for this step, if any.
    input_dataset: Dataset(s) this step consumes.
    output_dataset: Dataset this step produces.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_nameYes
descriptionYes
project_idNo
step_typeNo
codeNo
input_datasetNo
output_datasetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions recording lineage for reproducibility but does not disclose potential side effects, authentication needs, or what happens if a step is re-recorded.

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

Conciseness3/5

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

The description includes multiple sentences and a parameter list. It is adequately organized with the purpose first, but the parameter list makes it slightly long. Could be more concise.

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 7 parameters (2 required), no annotations, and an output schema, the description provides a reasonable overview of the tool's role in lineage tracking. It does not cover edge cases or error conditions, but is sufficient for basic understanding.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It provides brief explanations for each parameter (e.g., 'dataset_name: The dataset being transformed'), adding some meaning beyond the schema. However, explanations are minimal and could be more detailed.

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 records a cleaning/transformation step for dataset lineage. It uses specific verbs ('record', 'build') and explains the traceable chain purpose. However, it does not explicitly distinguish from sibling tools like 'clean_dataset' or 'profile_dataset'.

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 says 'Call once per step' and lists step types (recode, filter, join, etc.), implying when to use. But it does not provide explicit guidance on when not to use or alternatives.

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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