validate_dataset

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

No annotations are provided, so the description carries the burden. It does disclose that the tool performs a pass/fail validation gate and gives categories of checks, but it doesn't state what happens on failure, whether it writes anything, or what the response looks like.

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?

Two compact sentences that front-load the purpose and end with the critical workflow constraint. No filler or repetition.

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?

With no output schema or annotations and six parameters, the description is too thin: it omits return format, failure behavior, and how the parameters map to the validation checks. It gives the workflow position, but not enough for an agent to call it confidently.

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% and the description names none of the six parameters or their roles. The agent must infer that dataset_id, date_column, target_column, and channel_columns are required and how dims/control_columns affect checks; nothing in the description compensates.

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?

States a specific action ('Run') on MMM-specific checks (data quality, panel-shape, identifiability) and clearly identifies the resource. The closing 'must pass before fitting' links it to the fit_mmm workflow, distinguishing it from fitting and diagnosis siblings.

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?

Explicitly says this must pass before fitting, giving a clear temporal usage context. It doesn't name alternatives or exclusions, but the before-fitting gate is enough to route an agent.

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