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

No annotations are provided, so the description carries the full burden, and it does disclose the returned fields (columns, numeric flags, row count, provenance banner). However, it never states that the call is read-only/side-effect free, nor whether results are static or cached, which matters for an agent deciding how often to call it.

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 sentences, no filler. The payload is front-loaded and the second sentence is a genuinely useful routing instruction rather than padding.

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 must sketch the return value, and it does so with a four-part enumeration covering the key fields. It is complete enough for a zero-parameter read tool, missing only notes on read-only safety and result stability.

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 takes zero parameters, so the schema has nothing to document and there is no parameter semantics to add; the baseline of 4 applies.

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?

Names the concrete resource and enumerates exactly what comes back: columns, which are numeric, row count, and the provenance banner of the Historia Pojazdu VIN dataset. It is clear this is schema introspection, though it does not explicitly distinguish itself from the sibling dataset_provenance, whose domain the 'provenance banner' overlaps.

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?

Gives explicit ordering guidance with 'Call this first to learn the schema,' which tells the agent when in a workflow to reach for it. It stops short of naming alternatives or exclusions (e.g., when to prefer dataset_stats or dataset_provenance instead).

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