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

With no annotations, the description carries the full behavioral burden. It discloses the returned contents (columns, numeric flags, row count, provenance banner), which implies a read-only introspection call, but it never states permissions, cost, caching, or that it is side-effect free, so key behavioral traits remain inferred.

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 short sentences with the returned artifacts front-loaded and the calling instruction second; nothing is wasted. The enumeration in the first sentence is slightly clunky but functionally efficient.

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 discovery tool with no output schema and no annotations, the description supplies the key missing information: an inventory of what comes back and the directive to call it first. That covers the agent's decision needs, though a hint about output shape or relation to sibling provenance/stat tools would make it fully self-sufficient.

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 there is no parameter semantics to document and the baseline of 4 applies. The description correctly does not invent parameter detail.

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 names a specific verb-less but concrete introspection result set: columns, which are numeric, row count, and the provenance banner for the 'Taxooor dataset'. This is enough for an agent to know it is a schema-discovery call, though it does not explicitly disambiguate from siblings like dataset_stats or dataset_provenance, which appear to overlap on the row-count/provenance content.

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' gives an explicit sequencing rule and a clear purpose trigger. It does not, however, state when NOT to use it or point to an alternative when a different facet (e.g. stats or provenance) is wanted, so the routing guidance stops short of the top score.

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