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

With no annotations, the description carries the burden of behavioral disclosure. It does state what the tool returns and implies a read-only discovery operation, but it does not explain the exact form of the output or what 'provenance banner' means, leaving some ambiguity.

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 short and front-loaded with the most important returned information. It uses no filler, though the phrase 'provenance banner' could be clearer and slightly harms the otherwise concise structure.

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?

The tool has low complexity with no parameters, but there is no output schema, so the description is the only source of return-value information. It lists the main outputs but leaves 'provenance banner' undefined and does not describe the output format, which is a meaningful gap for an exploratory tool.

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 empty, so there are no parameter semantics to document. The 0-parameter baseline of 4 applies here, and the description does not need to compensate for any schema gaps.

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 tool's purpose: returning columns, their numeric status, row count, and provenance banner, with the explicit directive to call it first to learn the schema. It is distinguishable from most siblings, though its mention of a provenance banner slightly overlaps with the dataset_provenance tool.

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 explicitly says 'Call this first to learn the schema,' giving clear positional guidance for when to use it. It does not mention exclusions or alternatives, so it stops short of a full when-to-use/when-not-to-use explanation.

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