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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the specific information returned (columns, numeric flags, row count, provenance banner), which is a clear behavioral specification. It implies a read-only operation by describing it as learning the schema, though it doesn't explicitly state that no side effects occur. This is adequate given the tool's simplicity.

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 extremely concise: two sentences that front-load the key content (columns, numeric flags, row count, provenance banner) and immediately give the usage directive. There is zero wasted wording, and 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 tool has no parameters, no output schema, and a simple introspective purpose, the description is nearly complete. It states exactly what information will be returned and when to call it. A slight gap is that it doesn't describe the format of the provenance banner or how the numeric flags are represented, but for a schema-introspection tool this is minor and not blocking.

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 for this dimension is 4 per the rubric. The empty schema already indicates no inputs are required, and the description adds no parameter-related information. Since there are no parameters to document, a 4 is appropriate.

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, numeric flags, row count, and provenance banner. It names the specific dataset (Subbielane) and indicates it's a schema-learning tool, which distinguishes it from the sibling tools that compare, search, or compute stats. However, it does not explicitly contrast with siblings like dataset_provenance or dataset_stats, so it's clear but not fully differentiating.

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 usage guidance: 'Call this first to learn the schema.' This tells the agent when to invoke it, establishing it as the initial step. It does not mention when not to use it or name alternative tools, but the instruction is clear and contextually relevant.

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