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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose meaningful behaviors: handling of grouping commas and currency, and exclusion/counting of non-numeric rows. However, it does not mention whether the tool is read-only, how errors are handled (e.g., missing column), or the exact format of the response. While the data-cleaning behavior is useful, the description is not fully transparent about all behavioral traits.

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 that front-loads the core purpose (the list of statistics) and then adds two important behavioral notes (handling of commas/currency and exclusion of non-numeric rows). There is no wasted wording, and every part of the sentence contributes value. It is appropriately sized for a simple one-parameter tool.

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?

Given the tool's simplicity (one parameter, no output schema), the description covers the main operation and data-handling nuances. However, it omits the output format (e.g., whether it returns an object with keys like count, min, max, etc.) and does not address error conditions or edge cases (e.g., what if the column does not exist). Since there is no output schema, the description should have specified the return shape 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%, so the description must compensate. It implicitly refers to a 'numeric column' of the dataset, which likely corresponds to the 'column' parameter, but it does not explicitly state that the parameter is the column name or that it must be a column from the HostingByStack dataset. The description adds some meaning beyond the bare schema (which only has a minLength constraint), but it leaves the parameter's exact role and constraints somewhat implicit.

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's purpose: it computes count, min, max, mean, median, and sum for a numeric column of a specific dataset (HostingByStack). This is a specific verb+resource combination that distinguishes it from sibling tools like dataset_columns (listing columns) or dataset_row (fetching rows). The metrics are enumerated explicitly, leaving no ambiguity about what the tool returns.

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 implies the tool is used when summary statistics on a numeric column are needed, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or when not to use it. There is no reference to sibling tools or conditions that would route an agent to a different tool. The usage context is implied rather than stated.

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