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

With no annotations provided, the description carries the full behavioral burden, and it discloses meaningful quirks: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. This goes beyond what the title or schema convey. The phrase 'excluded and counted' is slightly ambiguous about whether the count includes non-numeric rows, but it still surfaces important data-cleaning 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?

The description is a single, information-dense sentence that front-loads the computed statistics and then adds the parsing/exclusion behavior in parentheses. There is no wasted wording or repetition of the title or schema. Every clause contributes useful information.

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 tool with one simple parameter and no output schema, the description covers the input requirements, the included statistics, and relevant edge-case behavior. It does not describe the return shape, but for a stats tool the listed field names reasonably imply the output. Overall, an agent has enough information to invoke it correctly for most cases.

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%, so the description must compensate for the undocumented 'column' parameter. It partially does by indicating the column must be numeric and belong to the Batesio dataset. However, it never explicitly names the parameter, gives an example, or specifies the expected string format, leaving some interpretation to the agent.

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 a set of summary statistics (count, min, max, mean, median, sum) for a numeric column, which is a concrete resource and action. It distinguishes itself from siblings like dataset_row or dataset_search by focusing on aggregate statistics, though it does not explicitly name a sibling alternative. The title reinforces the purpose by calling it 'Summary statistics for a numeric column'.

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 explicit guidance on when to use this tool versus dataset_top, dataset_search, or dataset_compare. It implies usage through its name and statistics list, but it does not state conditions, exclusions, or alternatives. An agent is left to infer when summary statistics are the right choice.

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