clean_transform

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

The description explicitly discloses the in-place mutation trait and gives exact formulas for each transform, which is good. However, with no annotations, it omits other important behavioral details such as reversibility, what happens to nulls, or whether the operation is destructive. The 'in place' phrasing helps but is a minimal disclosure for a mutation tool.

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 tight and efficient: one main sentence followed by a focused bullet list. Every line adds value, and the key information is front-loaded. No fluff or repetition.

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 transformation semantics are well-covered, and the existence of an output schema means return values are handled externally. Missing is any explanation of the alias parameter or caveats about in-place mutation (e.g., irreversibility). Overall, it covers the most complex aspect thoroughly but leaves a couple of secondary aspects unclear.

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?

The mapping parameter is exceptionally well explained, with each allowed string value accompanied by a formula and use case, adding significant meaning beyond the bare schema (which only says additionalProperties with string values). However, the alias parameter is not mentioned at all, and with 0% schema coverage, this leaves a gap. Still, the core parameter receives thorough treatment.

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 applies mathematical transforms to numeric columns in place, with a specific verb and resource. It also enumerates the transform kinds (log1p, sqrt, z_score, min_max), sharply distinguishing it from sibling cleaning tools like clean_drop_columns or clean_impute.

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?

The description provides clear context for when to use each transform (e.g., 'log1p... handles zeros, common for skewed counts', 'z_score... zero-mean, unit-variance'), enabling an agent to select the appropriate kind. It does not explicitly mention alternatives or when not to use, but the purpose context is strong enough for most decision-making.

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