preprocess_dataset

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

With no annotations, the description must disclose behaviors. It states 'apply preprocessing transformations' but does not indicate whether the dataset is modified in-place, if a new dataset is created, or any side effects. The output_name parameter suggests a new dataset, but this is not stated.

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 a single, clear sentence (4 words), making it very concise. However, it lacks structure and could benefit from additional context without losing conciseness.

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 complexity (4 parameters, nested object, many enum options) and no output schema, the description is too minimal. It does not explain the overall preprocessing pipeline or return value, leaving significant gaps for effective use.

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 coverage is 100%, so the schema itself documents parameters. The description adds no extra semantics beyond what the schema already provides (e.g., no explanation of when to use specific scaling or encoding methods). Baseline score of 3 is appropriate.

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 verb 'apply' and the resource 'preprocessing transformations to dataset', which distinguishes it from sibling tools like 'clean_dataset' and 'batch_process_datasets'. However, it could specify what types of transformations are included.

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 guidance on when to use this tool versus its many siblings (e.g., 'clean_dataset', 'validate_dataset'). The description does not mention prerequisites, alternatives, or context for selection.

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