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

No annotations are provided, so the description carries the disclosure burden. It does disclose the contents of the response (columns, numeric flags, row count, provenance banner) and implies a read-only role via 'learn the schema,' but it does not explicitly state that the tool is non-mutating, mention output format, or address auth/rate-limit considerations.

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, compact sentence that front-loads the specific outputs and ends with actionable guidance. There is no filler or redundant restatement of the title.

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 sufficient: it enumerates the return contents and positions the tool as the first call for schema discovery. An agent can invoke it immediately without missing essential context.

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 and the schema is an empty object, so the baseline is 4. The description adds useful context about what the agent will learn, but there are no parameters to elaborate on.

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 explicitly names the tool's outputs ('columns, which of them are numeric, the row count and the provenance banner') and states the intended use ('Call this first to learn the schema'). This makes the tool's purpose specific to schema/shape discovery and clearly distinguishable from siblings like dataset_search or dataset_compare.

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 clear positional guidance: 'Call this first to learn the schema,' implying this is the entry-point tool before others are used. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it falls short of full exclusion guidance.

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