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

No annotations are present, so the description carries the full burden for behavioral disclosure. It does not explicitly state read-only behavior or absence of side effects, though the schema-inspection nature strongly implies no mutations. A score of 3 reflects this missing explicit safety disclosure.

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 two sentences, front-loading the primary return components (columns, numeric flags, row count, provenance banner) before the usage directive. There is no redundancy, and every word adds value.

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?

With no output schema, the description compensates by listing all expected return components: columns, numeric indicators, row count, and provenance banner. This is sufficient for an agent to anticipate the result, though the provenance banner's structure remains underspecified.

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, making schema coverage trivially 100%. No parameter-specific detail in the description is needed, so the baseline 4 applies without adjustment.

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 the tool returns columns, numeric flags, row count, and a provenance banner for the Runsheetly dataset, reinforced by the directive 'Call this first to learn the schema' and the title 'Dataset columns and shape.' The return components are enumerated, though the exact output format is not fully specified.

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' provides explicit when-to-use guidance, implying it should precede other dataset operations. While it does not enumerate sibling tools, the directive gives adequate routing context to distinguish it from tools like dataset_row or dataset_stats.

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