remove_column

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses that the tool is destructive, that confirm defaults to false and produces only a preview, and it documents all three return shapes including the error case for unknown 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?

The description is front-loaded with the most important safety warning, then efficiently documents parameters, return values, and an example. Every section earns its place and nothing is redundant or vague.

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 destructive three-parameter tool with no annotations, the description is fully complete. It covers the safe workflow, required arguments, default behavior, return values, and an example invocation, leaving no critical gap for an agent 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 must explain parameters itself, and it does. It defines dataset as an exact name with an example, defines column as the column name to remove, and explains that confirm must be true to actually remove, contrasting with its default false.

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 states a specific verb and resource: 'Remove a column from a dataset,' and immediately distinguishes itself from related operations like add_column and update_column. The 'after explicit confirmation' qualifier further clarifies the tool's special destructive nature.

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 explicit procedural guidance: call with confirm=false first, show the user what will be lost, and only re-invoke with confirm=true after agreement. This clearly tells the agent when and how to use the tool, including the safe alternative flow.

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