parse_date_column

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

The notes explicitly disclose that the operation is destructive by default when targeting an existing column, and that omitting output_column overwrites the original column. This adds behavioral context beyond the destructiveHint annotation, such as the specific condition and the return summary.

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 a clear summary, followed by organized Args, Returns, and Notes sections. Every line contributes useful information with no redundancy or wasted words.

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 the tool's purpose, parameters, return value, and destructive behavior. This is sufficiently complete for a tool with 7 parameters and no output schema, though it could include an example or supported formats.

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 Args section provides brief descriptions for all seven parameters, which is crucial given the 0% schema description coverage. It clarifies the meaning of output_column, output_format, dayfirst, and header_row, though some definitions are terse.

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 first sentence clearly states the tool parses varying date formats in a column and writes normalized results to an output column. This specific verb+resource description distinguishes it from sibling tools like split_column or data_cleaner.

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?

No explicit when-to-use guidance or alternatives are provided. The description implies its use for normalizing dates but does not contrast with other column/data tools, leaving usage to be inferred from the purpose.

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