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

No annotations are supplied, so the description carries the burden, and it does disclose the payload contents (columns, numeric detection, row count, provenance banner). However it says nothing about whether the call is read-only/side-effect-free, cost, or output shape, which for an unannotated tool is a notable gap.

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, zero filler, with the returned content front-loaded and the usage cue trailing. Nothing could be cut without losing information.

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 and no annotations, the description is the only source of return-value and safety information, and it does enumerate the returned fields well. It could be more complete by stating that the operation is a read and by distinguishing its output from dataset_provenance's.

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 for the description to explain; 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?

The description names the concrete outputs (columns, numeric flags, row count, provenance banner) and frames the tool as schema discovery, which makes its job clear. It does not explicitly contrast itself with dataset_provenance or dataset_stats, both of which overlap on the provenance and stats 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 explicit sequencing guidance, which is more than most definitions offer. It stops short of saying when not to use it or which sibling to choose once the schema is known.

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