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

With no annotations, the description carries the full burden, and it does disclose real behavioral traits: currency and comma grouping are normalized, and non-numeric rows are excluded but counted. It says nothing about permissions, behavior on an all-non-numeric column, or the response shape, which is a gap for an unannotated tool.

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. The parenthetical earns its place by adding normalization and exclusion semantics, though the sentence packs a lot into a single clause chain.

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 annotations, so the description usefully enumerates the returned values and the edge-case handling, which is what an agent needs to interpret results. It remains incomplete only on parameter format and any value-shape caveats.

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 parameter 'column' has 0% schema description coverage, so the description must compensate. It does constrain the domain to numeric columns of one named dataset, but it gives no guidance on expected naming, case sensitivity, or how to discover valid column names (a sibling, dataset_columns, appears to exist for that).

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 returned (count, min, max, mean, median, sum) and the exact resource (a numeric column of the Contractor Lead Quotes dataset), so the purpose is unambiguous. It does not differentiate itself from siblings such as dataset_top or dataset_compare, which keeps it below 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?

There is no statement of when to use this tool versus dataset_top, dataset_compare, or dataset_search, nor any preconditions. The phrase 'numeric column' implicitly narrows applicability, but the agent must infer the rest.

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