record_dataset_treatment

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

No annotations are provided, so the description must carry the full burden. It states the tool records a step for lineage, implying a non-destructive add operation. However, it does not disclose side effects like whether it overwrites previous recordings, what happens if the dataset doesn't exist, or any required permissions. The behavioral information is adequate but not comprehensive.

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 structured with a purpose paragraph followed by an 'Args:' list, which is clear and front-loaded. However, the phrase 'Build a traceable chain raw → cleaned → analysis dataset' is somewhat poetic and could be more concise. The list repeats parameter names already in the schema, but it still adds value. Overall, it is appropriately sized without major redundancy.

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 (7 parameters, lineage tracking), no annotations, and no output schema provided despite 'Has output schema: true', the description is reasonably complete for input but lacks explanation of return values or what the tool outputs. It does not mention prerequisites (e.g., dataset existence) or error cases. The missing output info is a notable gap.

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 compensate. It includes an 'Args:' section that describes each parameter with context (e.g., step_type gets enumerated examples). This adds significant meaning beyond the schema property titles. Even though descriptions are brief, they are sufficient for an AI agent to understand parameter roles, especially for required fields like dataset_name and description.

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 'record' and the resource 'cleaning/transformation step for a dataset'. It explains the purpose of building a traceable lineage for reproducibility. This distinguishes it from siblings like clean_dataset which actually execute transformations. The phrase 'Call once per step' and listing example step types further solidifies its purpose.

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 provides clear context: 'Build a traceable chain raw → cleaned → analysis dataset' and 'Call once per step'. It implies usage in a pipeline for recording individual steps. However, it does not explicitly state when not to use it or compare directly with siblings such as clean_dataset or profile_dataset, which would enhance guidance.

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