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

With no annotations, the description carries the full burden and does useful work: it discloses data-handling rules (grouping commas and currency are normalized, non-numeric rows are excluded and counted) rather than just implying them. It still omits error behavior for an invalid column name and does not explicitly state that the operation is a read-only 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?

One dense, front-loaded sentence that lists the outputs first and appends the caveats afterward. Every clause earns its place with no filler.

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?

There is no output schema or annotation coverage, so the description must supply everything. It usefully enumerates the returned metrics and the data-cleaning semantics, but leaves out how to identify the column and how errors are surfaced, which matters for a tool with a single untyped string parameter.

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?

Schema description coverage is 0% for the single required 'column' parameter, so the description must compensate. It only says the column is numeric; it never explains whether the value is a header name, index, or case-sensitive label, leaving the agent to guess the correct identifier format.

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 the exact metrics computed (count, min, max, mean, median, sum) against a specific resource (the Miniature Paints Compare dataset column), so the agent knows precisely what it gets back. It does not explicitly contrast itself with siblings like dataset_columns or dataset_row, but the metric list is specific enough to distinguish it in practice.

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?

Usage is implied rather than stated: by saying stats apply to a 'numeric column', it signals the precondition that the column must contain numbers. There is no explicit when-to-use versus dataset_columns/dataset_top, and no statement about what happens if the agent points it at a non-numeric column.

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