analyze_dataset

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

No annotations are provided, so the description must disclose behavioral traits. It doesn't state whether the tool is read-only, how it handles errors, or any side effects. The phrase 'return actual results' is vague and adds no meaningful behavioral context.

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 brief, with a one-sentence summary followed by a clear Args list. It wastes no words and the structure is easy to scan.

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 that an output schema exists, return value details may not be needed. However, the description lacks context about when to use this tool versus siblings, and it doesn't explain differences between analysis types or consequences of choosing one. It is minimally sufficient but not comprehensive.

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 schema has 0% description coverage, so the description compensates well by explaining each parameter: dataset_path format (CSV), approved analysis_type values, and the meaning of columns (None means all). This is valuable beyond the bare schema.

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 tool's action ('Analyze dataset') and resource (dataset), with the Args section specifying supported analysis types (summary, correlation, distribution, missing_values). However, it does not differentiate from sibling tools like statistical_summary, which likely covers a subset of this functionality.

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 explicit guidance on when to use this tool versus alternatives. The description only documents parameters and provides a generic 'Analyze dataset and return actual results' without mentioning prerequisites, exclusions, or sibling tool comparisons.

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