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

No annotations are provided, so the description carries full responsibility for behavioral context. It describes what information is returned and that the call should come first, but it does not explicitly state that the operation is read-only, stateless, or free of side effects; the introspection-like contents make this mostly implicit.

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 two short sentences with no filler. The returned content is front-loaded and the usage directive appears cleanly at the end, so every word contributes value.

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?

With no annotations and no output schema, the description compensates by listing the main return contents and giving an explicit first-step instruction. It is sufficient for a simple zero-parameter introspection tool, though it could add output shape details or explicitly note that no arguments are needed.

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 input schema is already complete and there are no parameters needing clarification. The baseline for a zero-parameter tool is 4, and the description adds useful context by naming the specific dataset and its purpose.

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 the tool as returning the dataset's columns, numeric column flags, row count, and provenance banner, and it names the specific 'Subsroom dataset' resource. It does not explicitly differentiate itself from siblings like dataset_provenance or dataset_stats, so it falls just short of a 5.

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' provides a clear directive for when to use the tool as the initial discovery step. It does not mention when to prefer siblings such as dataset_search or dataset_stats, but the stated first-step context is clear enough.

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