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 non-obvious behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. It does not state permissions/read-only nature or what error surfaces if the column is entirely non-numeric, but the parsing and exclusion semantics are the key traits an agent needs.

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 dense sentence, front-loaded with the returned statistics and followed by the parsing/edge-case clause. No filler or repetition of the name or title.

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?

With no output schema, the description usefully enumerates the returned metrics (count, min, max, mean, median, sum), and it covers the main data edge case. Minor gaps remain: no mention of result ordering, error behavior for a fully non-numeric column, or how the excluded-row count is surfaced.

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 there is one required parameter, so the description must compensate. It does: the 'column' argument must be a numeric column of the TelescopeCompareHQ dataset, which tells the agent what values are valid beyond the bare string type in the schema.

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 states a specific verb set (count, min, max, mean, median, sum) and resource (a numeric column of the TelescopeCompareHQ dataset), so the operation is unambiguous. It does not explicitly contrast itself with siblings like dataset_top or dataset_columns, but its purpose is clear enough to select it without opening the schema.

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 implied rather than stated: the tool applies to a numeric column of a specific dataset. There is no explicit guidance on when to prefer this over dataset_top, dataset_columns, or dataset_search, and no stated preconditions or error cases for a non-numeric column.

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