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

With no annotations and no output schema, the description carries the burden and largely meets it by enumerating the returned fields (columns, numeric classification, row count, provenance banner). It is implicitly a read-only inspection call with no parameters, so there is little risk surface left undisclosed, though permissions or caching behavior are unmentioned.

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 delivers the full return inventory, the second delivers the usage cue. Every clause earns its place and the most actionable guidance (call first) is not buried.

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?

Because no output schema exists, the description must convey return contents, and it does so at field granularity. What it omits is the shape/format of that output (e.g., ordering, how 'numeric' is flagged), which is a minor gap for a zero-parameter introspection tool.

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 input schema has zero parameters, so there is nothing for the description to disambiguate; baseline is 4. No misleading parameter information is present.

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 resource (the Answering Service Quotes dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. It is distinguishable from most siblings, though the overlap with dataset_provenance (banner) and dataset_stats (row count) is not explicitly disambiguated.

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 that tells the agent when to reach for this tool ahead of the others. It does not, however, name which sibling to use afterward or what to do if the schema is already known.

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