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

With no annotations provided, the description carries the full burden of explaining behavior. It states exactly what information is returned and implies a read-only introspection operation via 'learn the schema.' It does not explicitly disclaim side effects, but the zero-parameter metadata nature makes that a minor gap.

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 with no filler: the first defines the returned content, the second states the intended invocation order. 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 supplies both the behavioral contract and the usage context. An agent knows what to expect—columns, numeric flags, row count, provenance banner—and when 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 there is nothing for the description to explain. Per the baseline for parameterless tools, this receives a 4.

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's output—columns, numeric indicators, row count, and provenance banner—and frames it as the schema-learning entry point for the Walkvo dataset. 'Call this first to learn the schema' distinguishes it from sibling tools like dataset_search, dataset_stats, and dataset_provenance.

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 gives explicit timing guidance: 'Call this first to learn the schema.' This tells the agent when to use it relative to other dataset tools. It does not enumerate exclusions or explicitly name alternatives, so it stops short of a 5.

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