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

No annotations are provided, so the description carries the burden of disclosing behavior. It does so by enumerating the returned content: columns, numeric columns, row count, and provenance banner. It does not describe output formatting or performance traits, but the central behavior is clear.

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 with no filler. It front-loads the primary output content and ends with a direct usage instruction, making it easy for an agent to parse quickly.

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 metadata inspection tool, the description gives enough context: what information is returned and the suggested first-call ordering. It does not detail the exact response format, but no output schema exists and the itemized list is reasonably complete.

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 parameter semantics are inherently satisfied; the baseline for no-parameter tools is 4. The description adds no parameter confusion and the schema confirms an empty parameter set.

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 column indicators, row count, and the provenance banner. The phrase 'Call this first to learn the schema' gives it a distinct role among siblings, though it does not explicitly contrast it with dataset_provenance or dataset_stats.

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 a clear usage cue: call this first to learn the schema. It does not explicitly name alternatives or say when not to use it, but the 'call this first' instruction provides clear context for when the tool is appropriate.

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