Normalize Features

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

With no annotations, the description carries the full behavioral burden. It discloses that a min-max fit is performed (returning fitted minimum and maximum values), but does not say whether the dataset is modified in place or a new one is returned, whether the fit is stored for later reuse, or what happens to non-numerical columns.

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

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

Front-loaded summary sentence followed by compact Args/Returns sections; the scaling method and default range are stated first. The per-parameter type restatements are slightly redundant but each parameter line earns its place given zero schema coverage.

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

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

There is no output schema, and the description gives only that a dict of normalized columns plus fitted min/max is returned, without the concrete key structure. For a transforming tool it also omits the in-place vs. copy behavior and whether the fitted range can be reused, which are the main things an agent needs to call it correctly.

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

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

Schema description coverage is 0%, so the description is the only source of parameter meaning, and it documents all four parameters including defaults. It adds useful semantics beyond the schema types: dataset_id may identify a split, columns is optional and defaults to all numerical columns, and feature_min/feature_max set the target range.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific operation and scope: min-max scaling of numerical columns into a fixed range, 0 to 1 by default. The verb+resource are unambiguous, but it never distinguishes itself from the closely named siblings scale_features and standardize_features, so an agent cannot tell which normalization variant to pick from the description alone.

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

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

Usage is only implied: it tells you that omitting `columns` normalizes all numerical columns, and that dataset_id may be a dataset or a split. There is no explicit when-to-use or when-not-to-use guidance and no pointer to standardize_features/scale_features as alternatives, leaving selection between normalization tools to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.