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

With no annotations, the description carries the behavioral transparency burden. It discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded but counted—useful quirks beyond the tool's name. It does not cover edge cases like empty columns or invalid column names, but it provides meaningful context.

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?

The description is a single compact sentence with the key output statistics front-loaded and behavioral caveats in a parenthetical. Every word contributes value; no fluff.

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 read-only tool, the description adequately covers purpose, output values, and key behavioral nuances. The lack of an output schema is mitigated by listing the statistics returned. It falls slightly short only by omitting error/edge-case handling and explicit sibling differentiation.

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?

The schema only defines 'column' as a non-empty string with 0% description coverage. The description adds that the column must be numeric and belong to the RentRollDesk dataset, which is minimal but helpful. It does not provide examples, formatting expectations, or error behavior for invalid columns.

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 clearly states the tool computes named statistics (count, min, max, mean, median, sum) for a numeric column of the RentRollDesk dataset. It specifies the resource and scope, and the specificity differentiates it from siblings like dataset_search or dataset_top.

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: call this when you need summary statistics for a numeric column in RentRollDesk. However, there is no explicit guidance on when not to use it or how it compares to sibling tools like dataset_compare or dataset_columns.

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