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 disclose non-obvious data handling: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. However, it says nothing about what happens if the requested column is non-numeric (error vs. empty result), permission requirements, or whether the excluded-row count is surfaced in the result.

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 sentence, front-loaded with the returned metrics, followed immediately by the data-handling caveats in parentheses. Every clause carries information and nothing is redundant, though the parenthetical makes it a dense single sentence.

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, the description usefully enumerates the returned statistics, and with no annotations it documents the parsing and row-exclusion behavior. The remaining gap is discoverability of valid column values and error behavior for non-numeric input, which are minor for a one-parameter read 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 single 'column' parameter has 0% schema description coverage, so the description must compensate. Saying 'a numeric column' usefully constrains the parameter to numeric fields, but it does not clarify whether the value is a column name or index, casing rules, or where a valid column list can be obtained (e.g., from dataset_columns).

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 states a specific verb (compute) and resource (summary statistics) and enumerates the exact outputs: count, min, max, mean, median, sum for a numeric column of a named dataset. An agent can tell what it does at a glance. It does not explicitly contrast itself with siblings like dataset_columns or dataset_top, so it stops short of a 5.

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: it applies to a numeric column of the Structured Settlement Compare dataset, which implicitly rules out non-numeric columns. No sibling is named as an alternative and there is no explicit when-to-use or when-not-to-use statement. This is minimum-viable implicit guidance rather than real routing help.

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