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

With no annotations, the description carries the full burden. It transparently discloses the output contents (columns, numeric flags, row count, provenance banner) and its role as the schema-introduction tool. It doesn't explicitly state read-only behavior, but the nature of the output makes that obvious. It adds useful context about being a first-step call.

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, no filler. The first sentence front-loads the core output (columns, numeric flags, row count, provenance banner) and the second adds a clear usage directive. Every word 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 tool with no output schema, the description fully covers what it returns and when to call it. It specifies the exact dataset and the purpose of the call. Nothing an agent needs to correctly invoke it is missing.

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 schema is empty and there is nothing to explain. Per the rubric, a baseline of 4 applies for tools with no parameters. The description doesn't need to add parameter information, and it doesn't try to invent any.

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 states what the tool does: it returns the columns, numeric flags, row count, and provenance banner of a specific dataset. It also explicitly frames this as the schema-learning entry point, distinguishing it from siblings like dataset_provenance or dataset_stats that serve different purposes.

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 phrase 'Call this first to learn the schema' provides explicit guidance on when to use this tool, establishing it as the initial discovery step. It doesn't explicitly contrast against alternatives, but the context and sibling names make the intended usage clear enough.

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