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 disclose what the tool produces: columns, numeric indicators, row count, and provenance banner. The read-only nature is strongly implied by 'learn the schema' and the zero-parameter design, though it never explicitly states that no data is modified.

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 short sentences with no filler. The output contents are front-loaded, and the usage instruction is placed at the end. Every part of the description 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?

For a zero-parameter tool with no output schema, the description is largely complete: it names the dataset, the returned information, and the intended call order. It doesn't describe the exact representation of non-numeric columns or the provenance banner, but that is a minor gap given the tool's simple introspection purpose.

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 parameter semantics to explain. The 0-parameter case earns the baseline of 4, and the description adds useful context about what the returned schema information will contain.

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 identifies the resource (the Clauselane dataset) and the key outputs: columns, numeric flags, row count, and provenance banner. It lacks an explicit verb like 'returns' or 'lists,' and it doesn't name sibling alternatives, but 'Call this first to learn the schema' positions the tool distinctly as the schema entry point.

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?

'Call this first to learn the schema' gives explicit timing guidance, telling the agent to invoke this tool before working with the dataset. It does not mention alternatives or when not to use it, but for a zero-parameter schema-introspection tool, this is clear context.

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