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

With no annotations, the description carries the behavioral disclosure burden. It clearly communicates this is a read-oriented schema discovery call and enumerates the returned information. It does not describe potential latency or side effects, but none are expected for a no-parameter metadata tool, and nothing in the description suggests mutation or hidden 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?

The description is two sentences with no filler. It front-loads the concrete outputs in the first sentence and gives the key usage directive in the second. Every word adds 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?

For a zero-parameter, no-output-schema tool, the description provides a complete picture of what the agent will receive: columns, numeric indicators, row count, and provenance banner. It could further explain how this differs from dataset_provenance and dataset_stats, which also touch provenance and counts, but the 'call first' framing largely resolves that ambiguity.

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 does not need to explain parameter meaning because there are none to document. The schema is empty and fully covered, so there is no semantic gap for the description to fill.

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 for the Nofovo dataset. It also frames the tool as the schema-learning entry point, which distinguishes it from siblings like dataset_row or dataset_stats. The imperative 'Call this first' makes its purpose unmistakable.

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 description gives explicit timing guidance with 'Call this first to learn the schema.' It clearly positions this as the initial discovery step before other dataset tools. It does not explicitly mention when not to use it or name alternatives, but for a zero-parameter metadata tool the context is clear.

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