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

No annotations are provided, so the description carries the full burden. It implies a read-only introspection call and discloses what is returned, but says nothing about output size, cost, or any constraints on repeated calls. Adequate but not rich.

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 zero waste, and the return contents are front-loaded ahead of the usage instruction. The phrasing 'The columns, which of them are numeric...' is slightly awkward as a sentence fragment but remains 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?

With no output schema and no parameters, the description correctly enumerates the return payload, giving the agent enough to know what it will receive. Nothing essential is missing for a simple no-argument 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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-level gaps exist to compensate for.

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?

States a specific resource (the Send A Parcel Compare dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and provenance banner. It is clearly an introspection/shape tool and distinguishable from siblings like dataset_search or dataset_stats, though the 'provenance banner' element slightly overlaps with dataset_provenance.

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?

Gives explicit ordering guidance: 'Call this first to learn the schema,' which tells the agent when to reach for this tool before the other dataset_* tools. It does not name a specific alternative or state when-not to use it, so it falls short of a full 5.

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