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 genuinely useful data-handling behavior: group separators and currency symbols are normalized, and non-numeric rows are excluded but still counted. It stops short of stating whether the call is read-only, what the response looks like, or how errors (missing/non-numeric column) surface.

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 front-loaded sentence that leads with the returned statistics and drops edge-case handling into a parenthetical, with no filler. Slightly dense but every clause earns its place.

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 output schema and no annotations, the description usefully enumerates the exact metrics returned, which substitutes for the missing return contract, and it covers null/format handling. Missing only error behavior and dataset/connection context, which are minor at this complexity.

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 coverage is 0% and the schema only gives a string type with minLength 1, so the description's constraint that the column must be numeric is real added meaning. However it offers no column naming convention, example values, or pointer to dataset_columns for discovering valid names, leaving the parameter under-specified.

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 (compute count/min/max/mean/median/sum) against a specific resource (a numeric column of the Injection Molding Cost Checker dataset), so an agent immediately knows it is an aggregation tool. It is implicitly distinct from listing siblings like dataset_rows or dataset_columns, but it never explicitly contrasts itself with dataset_top, the nearest aggregation-ish sibling.

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 when-to-use or when-not-to-use guidance and no named alternative. The agent must infer from the stat list that this is the tool for numeric summaries, and nothing tells it what to do when the column is non-numeric or which sibling to consult for valid column names.

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