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

With no annotations, the description carries the full behavioral burden. It discloses the concrete return contents and frames the operation as inspecting, not mutating, the Sudslane dataset; that is sufficient for a zero-parameter metadata query, though it does not explicitly say 'read-only'.

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 short sentences carry all essential information, with the output contents front-loaded and the usage guidance ('call first') at the end. No filler or repetition exists.

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 no output schema and no annotations, the description compensates by listing the main returned elements and giving an ordering cue. It is complete for a simple schema-discovery tool, though it could briefly mention that it takes no arguments or allude to which sibling to use after schema discovery.

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 there is nothing for the description to document. The baseline of 4 applies because no parameter semantics are needed; the schema already reflects an empty input object.

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 exactly what the tool yields — columns, numeric-column flags, row count, and provenance banner — so an agent can tell this is a schema-inspection tool. It is clear but lacks an explicit verb such as 'returns' and does not explicitly contrast itself with sibling tools like dataset_stats or dataset_provenance.

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?

"Call this first to learn the schema" is an explicit when-to-use instruction and positions it ahead of the sibling dataset tools. It does not go further to say when not to use it or name a specific alternative, so it misses the top criterion.

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