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

With no annotations provided, the description carries the full burden. It discloses the concrete output contents (columns, numeric indicators, row count, provenance banner) and implies a non-mutating schema-inspection behavior through 'learn the schema.' No side effects or access concerns are mentioned, but the tool is a simple zero-parameter inspection.

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 two sentences, front-loads the key output contents, and ends with a practical instruction. Every clause earns its place with no 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?

For a zero-parameter tool with no output schema, the description sufficiently explains both the return value and how to use it. It covers the dataset name, what data will be returned, and the recommended first-step usage, making it adequate for an agent to call it correctly.

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 tool has zero parameters, so the baseline is 4. The description appropriately avoids inventing parameter details and focuses on the output, which is more relevant for a schema-discovery tool.

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 enumerates what the tool returns: columns, numeric flags, row count, and provenance banner for a specific dataset. It implies a schema-discovery purpose, though it does not explicitly distinguish itself from sibling tools such as dataset_stats or dataset_top.

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 instruction to 'Call this first to learn the schema' provides explicit timing guidance for when to invoke it. It does not state exclusions or directly name alternatives, but the sequencing guidance is clear and useful.

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