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

With no annotations, the description carries the behavioral burden and does so reasonably: it enumerates the returned elements and frames the call as a schema-discovery step. It never explicitly declares the operation read-only, but nothing about the described output implies mutation, and there are no parameters to cause side effects.

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 sentences, zero filler: the return contents lead and the recommended usage order follows. Every clause earns its place and the actionable instruction is front-loaded.

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?

For a zero-parameter introspection tool with no output schema, the description covers both what comes back and when to call it. The only gap is disambiguation from the overlapping row-count/provenance siblings.

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 the baseline is 4. There is nothing parameter-related for the description to clarify, and the empty schema is fully consistent with the description.

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 Wen Receipts dataset) and enumerates exactly what it returns: columns, numeric flags, row count, provenance banner. It is clear on its own, though it does not explicitly distinguish itself from siblings like dataset_stats or dataset_provenance that appear to overlap on row count and 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?

"Call this first to learn the schema" gives explicit ordering guidance for a pipeline of dataset tools. It stops short of naming alternatives or exclusions (e.g., when to prefer dataset_stats or dataset_provenance instead), so it is strong but not fully routing.

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