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

There are no annotations, so the description carries the behavioral disclosure burden. It clearly describes an inspection/metadata operation and lists the exact pieces of information returned. It implies non-mutating behavior through the phrase 'learn the schema', though it does not explicitly state that no data is modified.

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 sentences with no wasted words. The first sentence front-loads the output contents, and the second gives a clear usage directive. Every clause earns its place.

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 metadata tool with no output schema, the description fully covers what the agent can expect to receive and when to call it. It provides enough context for correct invocation without being verbose.

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 input schema has no parameters, so there are no parameter semantics to document. The phrase 'of the Disclovo dataset' clarifies that the tool operates on a fixed/predefined dataset. This matches the baseline 4 for a zero-parameter 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 identifies what the tool returns — columns, numeric columns, row count, and provenance banner — for the Disclovo dataset. It lacks an explicit imperative verb like 'Returns', but the intent is unmistakable. It also helps differentiate from sibling tools by framing this as the schema-learning first call.

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 description gives explicit usage timing: call this first to learn the schema. It does not explicitly name sibling alternatives or state when not to use it, but the sibling names and the 'first' instruction make the intended workflow reasonably clear.

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