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

No annotations are provided, so the description carries the full burden. It discloses the exact contents of the result set—columns, numeric flags, row count, and provenance banner—and implies a read-only schema-inspection operation. It does not address side effects or access requirements, but for a metadata tool this is sufficient.

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 redundant words. The first enumerates the output components; the second provides invocation context. 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?

Given the absence of an output schema, the description tells the agent what information will be returned and when to call. It might have included an example or clarified the output format, but the core context needed to invoke correctly is present.

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 input schema is empty, so there are no parameters to document. The description adds context about what the output contains, satisfying the baseline expected for a zero-parameter tool.

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 specifies exactly what the tool provides: column names, which are numeric, row count, and the provenance banner. The phrase 'Call this first to learn the schema' establishes it as the schema-discovery tool, distinguishing it from siblings like dataset_search or dataset_stats.

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 directive 'Call this first' gives clear ordering guidance for when this tool should be invoked relative to other dataset tools. It does not explicitly name alternatives or describe when not to use it, but the timing guidance is strong.

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