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

With no annotations, the description carries the full behavioral burden, and it does add non-obvious value: grouping commas and currency symbols are parsed, and non-numeric rows are excluded yet counted. However, it omits what happens if the column is entirely non-numeric, whether errors are raised, and how the excluded-row count is surfaced in a response with no output schema.

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?

A single sentence that front-loads the returned metrics and tucks the data-handling caveats into a parenthetical. No filler, though the parenthetical packs three distinct behaviors into one clause, slightly blurring their priority.

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 one-parameter tool with no annotations and no output schema, the description covers the essential behavior but leaves gaps: the shape of the returned statistics object, whether the excluded-non-numeric count is included in the result, and error behavior on a non-numeric column. Adequate but not fully self-sufficient.

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 does say the value must be a numeric column of the specific dataset, which constrains the input meaningfully, but it gives no naming format, no hint that a sibling (dataset_columns) enumerates valid column names, and no example.

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 operation (summary statistics) on a specific resource (a numeric column of the Sauna Cold Plunge Compare dataset) and even enumerates the returned metrics (count, min, max, mean, median, sum). It is clearly distinguishable in substance from siblings like dataset_top or dataset_search, though it never explicitly contrasts itself with them.

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 only implied: 'a numeric column' tells the agent the tool applies to numeric columns, and the handling notes suggest it tolerates messy formatting. There is no explicit when-to-use vs. when-not, no mention of alternatives such as dataset_top for ranking, and no prerequisites stated.

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