profile_dataset

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

With no annotations, the description carries the full burden. 'Rule-based' usefully implies deterministic, non-ML output, but nothing is said about cost, whether the full dataset is scanned, memory limits, or whether the operation is read-only. For a potentially expensive profiling pass this is a significant gap.

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?

A single dense fragment with no filler and the substance front-loaded. It is terse to the point of being a list rather than a sentence, but nothing is wasted.

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?

An output schema exists, so the enumerated findings arguably restate return content rather than adding context. The description therefore covers analysis scope but omits the input (which dataset) and any behavioral or usage context, leaving an agent able to guess the call but not to reason about it.

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 single parameter 'name' has 0% schema description coverage and the description never references it, not even to say it is the dataset identifier. Unlike the zero-parameter baseline, one undocumented required parameter leaves real ambiguity about what value to supply.

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 enumerates what the profiling produces (missing values, duplicates, outliers, correlations, skew, category extremes) but never states the action or resource in verb+resource form, and never mentions that it operates on a dataset. It is distinguishable from a generic describe only by inference; no sibling such as describe_dataset is contrasted.

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?

There is no when-to-use guidance at all, no statement of when profiling is preferable to describe_dataset or query_dataset, and no prerequisites or exclusions. Usage is only implied by the listed findings.

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