drop_columns

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that columns are dropped and gives an example. It does not disclose whether the operation is in-place or returns a new dataframe, what happens if a column doesn't exist, or any side effects. This is a significant gap for a mutation tool without annotations.

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 three sentences: a clear action, a guiding note, and a concrete example. It is front-loaded and every sentence earns its place. No fluff or unnecessary detail.

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 tool is simple and an output schema exists, so return values need not be explained. However, the description omits details about the 'df_name' parameter and the mutation behavior. The guidance is useful but not comprehensive. It is minimally viable for a simple tool, but the lack of side-effect disclosure and parameter completeness keeps it from being fully 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. The example clarifies the 'columns' parameter (list of strings), but the 'df_name' parameter is not mentioned at all. The description adds some meaning for 'columns' via the example but fails to provide any semantics for the optional 'df_name' parameter, leaving half the parameters undocumented.

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 action ('Drop specified columns from the dataframe') and the resource (dataframe columns). It distinguishes itself from siblings like drop_duplicates and drop_missing by specifying columns. The additional guidance on what types of columns to remove (constants, IDs, leaked features) further clarifies its purpose.

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 on when to use this tool: to remove useless columns like constants, IDs, and leaked features. It also cautions to 'always understand a column before dropping it'. However, it does not explicitly mention alternatives or when-not-to-use scenarios, so it falls short of a 5.

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