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Transform Feature

transform_feature

Applies a mathematical transformation to a numerical column to reduce skewness, optionally adding a new column or replacing the original, and reports before/after skewness.

Instructions

Apply a mathematical transformation to one numerical column, usually to reduce skewness. Args: dataset_id (str): ID of a dataset or of a split. column (str): Numerical column to transform. method (str): One of "sqrt" (needs values >= 0), "square", "cube", "reciprocal" (no zeros), "abs", "cbrt" (cube root, works with negatives), "signed_log1p" (log that keeps the sign, works with negatives), "yeo_johnson" (fitted power transform, works with negatives), "boxcox" (fitted, needs values > 0). Fitted methods learn their parameters from the training rows only. new_column (str, optional): Name of the new column. Default is "_". replace (bool): Overwrite the original column instead of adding a new one. Default is False. Returns: dict: The new column and the skewness before and after (measured on the training rows).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "column": "<column>",
      "method": "<method>"
    },
    "name": "transform_feature"
  }
}

Input Schema

Name	Required	Description	Default
`column`	Yes
`method`	Yes
`replace`	No
`dataset_id`	Yes
`new_column`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 14, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that fitted methods learn parameters from the training rows only (avoiding leakage), that `replace` overwrites the original column, and what is returned. It doesn't state whether the dataset is mutated in place versus copied, nor what error surfaces when a constraint (e.g., sqrt on negatives) is violated.

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?

Front-loaded with the one-line purpose, then a structured Args/Returns block. The method list is long but each entry earns its place via distinct value preconditions. Minor redundancy in restating defaults that already appear in the schema.

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?

For a 5-parameter mutation tool with no annotations and no output schema, the description covers parameters, constraints, defaults, and the return value (new column plus before/after skewness). Remaining gaps are minor: mutation semantics on the source dataset and failure behavior for invalid method/value combinations.

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?

Schema description coverage is 0%, so the description must carry all parameter meaning, and it does: dataset_id, column, every allowed `method` value with its domain constraints, the default naming pattern for new_column, and the semantics and default of `replace`. This adds substantial meaning beyond the bare types in the schema.

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 first sentence gives a precise verb and resource ('Apply a mathematical transformation to one numerical column') plus the intent ('usually to reduce skewness'). It is clear what the tool does, though it never names or distinguishes itself from the log_transform sibling, which overlaps with the 'signed_log1p' method listed.

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?

Usage context is implied by 'usually to reduce skewness' and by the per-method value constraints, which steer an agent toward a valid method. However, there is no explicit when-to-use/when-not guidance and no routing to log_transform or bin_feature for related preprocessing tasks.

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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