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

With no annotations provided, the description carries the full burden and delivers meaningful behavior: 'grouping commas and currency are handled; non-numeric rows are excluded and counted.' This discloses edge-case handling (format parsing and the dual exclusion/counting of non-numeric data) that the schema cannot convey. It stops short of describing the exact return shape, but for a read-only aggregator this is solid disclosure.

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 of roughly 25 words front-loads the deliverable (the six statistics) before trailing with qualifiers. Every clause earns its place — the formatting claim and the non-numeric row policy are compact and non-redundant. There is no filler.

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 low-complexity single-parameter tool with no output schema, the description is nearly complete: it states the computed statistics, the input expectations, and the edge-case handling. The one residual gap is that it never spells out the return format, though the enumerated stats reasonably imply the response fields. Overall adequate coverage for the tool's simplicity.

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 the useful constraint that the column must be numeric and references the GPA Grade Compare dataset, which the bare schema (a single string 'column') does not. However, it does not enumerate valid column values, and it never points to dataset_columns as the way to discover them, so it only partially compensates for the zero-coverage gap.

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 verb (compute) and a precise resource: 'count, min, max, mean, median and sum of a numeric column of the GPA Grade Compare dataset.' Listing the exact statistics removes any ambiguity about output scope. It also clearly differentiates from sibling tools like dataset_columns, dataset_row, and dataset_search, which all imply raw data access rather than aggregation.

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 gives no guidance on when to choose this tool over siblings. It never mentions that dataset_columns should be consulted to discover valid numeric columns, nor does it contrast with dataset_top or dataset_compare, leaving an agent to infer selection intent from the stats list alone.

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