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

With no annotations present, the description carries the full burden and does disclose non-obvious behavior: grouping commas and currency symbols are normalized, and non-numeric rows are silently excluded but accounted for in a count. It stops short of stating read-only nature, handling of nulls/empty columns, or error behavior.

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 tight sentence that front-loads the substantive content (the list of statistics) and appends the important caveats about formatting and excluded rows. No filler, though the parenthetical is slightly dense.

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 single-parameter tool with no output schema, the description effectively doubles as the return-value spec by enumerating the six statistics and noting that excluded rows are counted. Missing only edge-case behavior (empty/non-numeric-only columns) to be fully complete.

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% for the single 'column' parameter, so the description must compensate. It adds the key semantic constraint that the column must be numeric, but does not explain how to discover valid column names (e.g., via dataset_columns) or whether names are case-sensitive.

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 resource (a numeric column of the Trading Card Value Checker dataset) and enumerates the specific statistics returned (count, min, max, mean, median, sum), so an agent knows precisely what it computes. It is clearly distinct from siblings like dataset_row or dataset_search, though it never explicitly contrasts itself with them.

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: the constraint that the column must be numeric tells the agent when this tool is applicable, but there is no explicit when-to-use/when-not guidance and no mention of alternatives such as dataset_top or dataset_compare for non-aggregate needs.

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