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

With no annotations, the description carries the full burden and does disclose important behavioral traits: non-numeric rows are excluded and counted, and grouping commas/currency are handled. It does not state read-only safety explicitly, but the aggregate nature makes that clear enough.

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 parenthetically captures data-cleaning behavior. No wasted words.

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 simple one-parameter aggregate tool with no annotations and no output schema, the description lists the returned statistics and the data-handling rules. It is largely complete, though it omits output key structure and error behavior for an invalid column.

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%, so the description must compensate. It adds that the column must be a numeric column of this dataset, which is useful, but does not specify column-name format or examples.

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 specific operation (summary statistics) on a specific resource (numeric column of the Laser Materials Compare dataset) and lists the exact metrics returned. It distinguishes the tool from simple row retrieval or search, but does not explicitly contrast itself with siblings like dataset_top or dataset_columns.

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 description implies the tool is used to get aggregate stats for a numeric column, but gives no explicit when-to-use guidance, no alternatives, and no conditions for selecting it over sibling tools.

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