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

With no annotations, the description carries the behavioral burden. It usefully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not discuss error or edge-case behavior, but adds meaningful behavioral context beyond the obvious statistics computation.

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?

The entire description is one dense, front-loaded sentence with no redundant phrasing. Every clause contributes either to what is computed, which dataset is targeted, or how the data is preprocessed.

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 single-parameter aggregation tool, this is largely complete: it names the dataset, the input column type, the returned statistics, and key data-handling behaviors. It lacks explicit output formatting or error behavior, but those are not critical for correct invocation.

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 schema only defines 'column' as a non-empty string, so the description must compensate. It clarifies that the column must be a numeric column of the ReceivableLedger dataset and implies how currency/grouping formatting will be interpreted, which adds real semantic value.

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 precise operation — computing six named summary statistics on a numeric column of a specific dataset. This clearly distinguishes it from sibling tools like dataset_search, dataset_top, or dataset_row.

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?

The intended use is implied: call this when you need summary statistics of a numeric column in ReceivableLedger. However, it does not explicitly contrast with sibling tools or state when not to use it, so routing relies on inference.

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