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

No annotations are provided, so the description carries the full burden. It discloses what information is returned (columns, numeric flags, row count, provenance banner) but does not mention side effects, permissions, or the exact structure of the response. For a read-only metadata tool, this is adequate but not exhaustive.

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 packs all essential information: the resource (Enpso dataset), the specific outputs, and the recommended call order. It is front-loaded and free of filler.

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 parameters, no output schema, and no annotations, the description covers the core purpose and usage. It could be more explicit about the return format (e.g., structure of the columns list), but it provides enough for an agent to understand the tool's role and decide to call it.

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 effectively explains what the tool does without needing to reference any parameters, fulfilling the semantic role.

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 states exactly what the tool returns: columns, numeric flags, row count, and provenance banner. It also frames the tool as a schema-discovery entry point ('Call this first to learn the schema'), distinguishing it from data-access siblings like dataset_row and dataset_search.

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?

It explicitly tells the agent to call this first to learn the schema, giving clear when-to-use guidance. It does not explicitly name alternatives or when not to use it, but the instruction to call first implies it is a prerequisite for other operations.

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