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

With no annotations, the description carries the full behavioral disclosure burden. It reveals the data fields it returns (columns, numeric flags, row count, provenance banner) and implies a read-only metadata operation, but it does not explicitly state side-effect-free behavior, output formatting, or error behavior.

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 contain all essential information with no filler. The first sentence enumerates the return components that matter, and the second sentence gives the usage directive.

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 parameterless metadata tool, the description covers the main expected outputs and the primary usage timing. It does not describe the exact output structure or explicitly guarantee read-only behavior, but given the sibling set and the lack of an output schema, it is nearly complete.

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 takes zero parameters, so the description has no parameter meanings to add beyond the schema. Per baseline for no-parameter tools, 4 is appropriate; the description's mention that this is the schema-learning call adds mild contextual value.

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?

Description states a specific resource ('Ppmly dataset') and concrete returned content: columns, numeric flags, row count, provenance banner. It distinguishes itself from 'dataset_row', 'dataset_search', and 'dataset_stats' by framing its output as the schema overview to request first.

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 explicit instruction 'Call this first to learn the schema' gives a clear use case. It does not name sibling alternatives or when-not-to-use it, but with zero parameters and an overview purpose, the positioning as the initial schema call is sufficient context.

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