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

The description accurately discloses the tool's output (columns, numeric flags, row count, provenance banner) without any annotations. It implies a read-only operation but does not explicitly state side effects or error behavior; however, for a simple schema inspection tool, this level of transparency 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?

The description is a single, concise sentence that packs all necessary information: what it returns and when to use it. No fluff or redundancy, making it highly efficient for an agent to parse.

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 tool's simplicity (no parameters, simple output), the description fully covers its purpose and usage context. The instruction to call it first adds valuable context for sequencing with sibling tools, making it complete for its intended role.

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 no parameters, and the schema coverage is 100% (empty properties). The description adds no parameter details, but since there are none, the baseline of 4 applies. There is no gap in parameter understanding.

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 that the tool returns columns, numeric flags, row count, and provenance banner, and explicitly instructs to call it first to learn the schema. This makes the purpose unambiguous and distinct from sibling tools that handle other dataset operations.

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, positioning it as the initial step before other dataset actions. This is a clear usage directive that helps an agent decide when to invoke it.

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