normalize

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

The description discloses that scaling is in-place and explains the three methods, which is useful. However, it does not mention behavior with non-numeric columns, error handling, or the role of df_name. Without annotations, these unaddressed aspects leave some transparency gaps.

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?

The description is compact, front-loaded with purpose, then methods, usage guidance, and an example. Every sentence contributes value, with no redundant filler.

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?

The description covers purpose, methods, when-to-use, and includes an example. It lacks explanation of df_name and edge-case behavior, but given the output schema exists and the tool is a standard preprocessing step, it is reasonably complete.

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 must compensate. It thoroughly explains the 'method' parameter with definitions for minmax, standard, and robust, and clarifies that 'columns' refers to numeric columns. However, the 'df_name' parameter is not explained, so compensation is partial.

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?

The description clearly states the tool scales numeric columns in-place, distinguishing it from other preprocessing tools like log_transform or clip_outliers. The verb 'scale' and resource 'numeric columns' are specific and unambiguous.

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?

Explicit guidance is given on when to use normalization (distance-based models) and when not to (tree-based models), along with method-selection advice (use 'standard' by default, 'robust' if outliers remain). This directly addresses the decision process for the agent.

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