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

There are no annotations and no output schema, so the description carries the full behavioral burden. It does disclose the return contents (columns, numeric flags, row count, provenance banner), which is genuinely useful, but it says nothing about the response format, whether the fields are keyed by name, or any access constraints.

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: the first enumerates the payload, the second gives the call-ordering instruction. Nothing is redundant and the salient content leads.

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 does the work by stating what is returned, which is enough for an agent to decide to call it. It could still clarify the shape of the returned columns list and provenance banner, but nothing essential is missing for a zero-parameter schema call.

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 per the baseline there is nothing for the description to disambiguate. The absence of any parameter discussion is correct here rather than a gap.

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 concrete resource (the Asbestos Survey Cost dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. That is well beyond a tautology, but it does not differentiate itself from siblings like dataset_stats (row count/numeric) or dataset_provenance (provenance banner), so it stops 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?

It gives explicit ordering guidance — 'Call this first to learn the schema' — which clearly signals the tool's role as an entry point before other dataset_* calls. It does not, however, name an alternative or state when a sibling (e.g. dataset_stats) should be preferred instead.

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