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

With no annotations, the description carries the behavioral burden and it does well by enumerating the exact output contents: columns, numeric flags, row count, and provenance banner. It does not explicitly state 'read-only' or describe edge cases, but 'learn the schema' strongly implies a safe, non-mutating introspection 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, both meaningful. The first packs the tool's output into a compact list; the second provides actionable ordering guidance. There is no filler or redundant restatement of the title.

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 simple tool with no parameters and no output schema, the description covers the main things an agent needs: what information is returned and when to call it. It leaves minor ambiguity around the exact meaning of 'provenance banner' and does not describe the response structure, but that is unlikely to prevent correct invocation.

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 and the schema is therefore completely covered. Baseline for a zero-parameter tool is 4, and no parameter-specific explanation is needed or missing.

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 returns: columns, which are numeric, the row count, and the provenance banner, and frames it as the schema-learning entry point. 'Call this first to learn the schema' gives the tool a distinct role among the dataset_* siblings, even though some elements overlap with 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?

The instruction 'Call this first to learn the schema' gives explicit when-to-use guidance and positions it as the initial discovery step. It does not explicitly name alternatives or state when not to use it, but for a zero-parameter schema introspection tool, the usage 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.