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

With no annotations, the description carries the full burden, and it does disclose the read-only return contents (numeric-flag columns, row count, provenance banner), which is the main behavioral trait for this tool. However it says nothing about output format, whether a row limit applies, or error behavior on an unavailable dataset.

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 tight sentences with the returned payload front-loaded and the usage directive last. Every clause carries information; nothing is redundant.

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?

There is no output schema, so the description must convey the return shape, and it enumerates the four things returned, which is sufficient for an agent to decide to call it first. Minor gaps remain about format and sizing of the result.

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 are no parameter semantics to explain and the baseline of 4 applies. The description correctly does not waste words on inputs.

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 the exact payload returned (columns, which are numeric, row count, provenance banner) for a named dataset, so the agent knows precisely what resource this reports on. It does not explicitly contrast itself with siblings like dataset_stats or dataset_provenance, which overlap somewhat, so it falls short of a 5.

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 a clear usage directive and ordering relative to the other dataset_* tools. It does not state when not to use it or which sibling supersedes it for narrower questions, so no explicit alternatives are covered.

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