strip_column

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

With no annotations provided, the description carries full burden and does well by specifying the tool returns a 'ColumnOperationResult with strip details', indicating structured output. It clarifies that stripping applies to 'leading/trailing whitespace' by default and can target 'specific characters', though it doesn't cover error handling, performance, or side effects on the dataset.

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 core purpose, followed by a concise returns statement and practical examples that earn their place by demonstrating common use cases. No redundant or verbose sentences; it efficiently communicates key information in a structured format.

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?

Given the tool's moderate complexity (2 parameters, 100% schema coverage, output schema present), the description is mostly complete. It covers purpose, output type, and usage examples, but could improve by mentioning sibling tool distinctions or potential impacts on data integrity, though the output schema reduces the need for detailed return value explanations.

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 100%, so the baseline is 3. The description adds minimal value beyond the schema by illustrating parameter usage in examples (e.g., 'phone', '()' for chars), but doesn't explain semantics like character set handling or edge cases beyond what the schema's descriptions already provide.

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 specific action ('strip whitespace or specified characters') and target resource ('from column values'), distinguishing it from siblings like 'replace_in_column' or 'transform_column_case' which perform different transformations. It precisely defines the operation without being tautological.

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 implies usage through examples (e.g., cleaning currency values, removing quotes) but lacks explicit guidance on when to choose this tool over alternatives like 'replace_in_column' for character substitution or 'transform_column_case' for case changes. No when-not-to-use scenarios or prerequisites are mentioned.

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