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

With no annotations, the description carries the burden of behavioral disclosure. It states exactly what the tool returns (columns, numeric flags, row count, provenance banner), making the read-only nature and output scope transparent. It does not cover format details, but for a zero-parameter metadata tool this is sufficient.

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 a single sentence that front-loads the output contents and ends with a clear usage directive. Every phrase earns its place, with no filler or 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 simple zero-parameter introspection tool, the description provides the essential output items and the right invocation timing. The lack of an output schema is partially offset by the enumerated return values, though column count is implied rather than stated.

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, and the schema description coverage is effectively 100% with an empty parameter object. Per the baseline for zero-parameter tools, the description need not add parameter meaning, and it does not attempt to.

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 specifies the tool's output: the columns, which are numeric, the row count, and the provenance banner for the FlatRateBook dataset. It also gives a direct call-to-action ('Call this first') that distinguishes it as the schema-learning entry point among siblings like dataset_row and dataset_stats.

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 'Call this first to learn the schema' gives explicit usage context and implies this tool precedes the sibling analysis tools. It does not enumerate when not to use it or name alternatives, but the positioning is clear enough for an agent.

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