drop_low_importance

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

With no annotations, the description carries the full burden. It explicitly warns 'Destructive: actually drops columns,' and details what each method does (variance <= threshold, abs correlation <= threshold). However, it does not specify whether the operation modifies in-place or returns a new dataframe, nor what the output schema contains.

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 efficient: a one-line summary, a compact method list, a destructive warning, and an example. Every sentence adds value and the structure is easy to scan.

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?

For a tool with 4 parameters and zero schema/annotation support, the description covers the core semantics, destructive behavior, usage timing, and an example. Minor gaps remain around df_name and the return value/result format, but the overall picture is clear enough for an agent to use 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 compensates by explaining the meaning of 'method' values and 'threshold' behavior, and providing an example that maps target_column, method, and threshold. It does not explain the 'df_name' parameter, which has a default but may operate on the current dataframe.

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 uses a specific verb ('Drop') and a resource ('columns') with a clear thresholding condition. It distinguishes itself from sibling tools by explaining the two methods (variance, correlation) and explicitly referencing correlation_filter/variance_filter as prior review steps.

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 gives direct usage guidance: 'Use AFTER reviewing results from correlation_filter or variance_filter'. It also provides a concrete example call, clarifying when and how to invoke the tool.

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