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

With no annotations provided, the description carries the full burden. It discloses key behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This adds value beyond the schema. It does not specify the return format or error handling, but for a simple stats tool, the disclosed behaviors are significant.

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, front-loaded sentence that lists the statistics first, then adds the data-handling notes. No unnecessary words or repetition. It is concise and effective.

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 simple tool with one parameter and no output schema, the description covers the main points: what it computes and how it handles data quirks. It could mention the return format (e.g., a JSON object with those fields) or error behavior, but it is sufficient for an agent to invoke it correctly in 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 description coverage is 0%, so the description must compensate. It clarifies that the 'column' parameter refers to a numeric column of the dataset, adding meaning beyond the plain string type. It does not specify case sensitivity or exact matching, but given a single parameter, it provides adequate context.

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 clear verb (compute summary statistics) and resource (a numeric column of the Mandatzo dataset), listing specific statistics (count, min, max, mean, median, sum). This distinguishes it from siblings like dataset_row or dataset_search, which handle raw data retrieval, so an agent can immediately identify its purpose.

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 usage for numeric column analysis but does not explicitly state when to use this tool over siblings (e.g., 'use for summary statistics, not raw row access'). No alternatives or exclusions are mentioned, leaving some inference to the agent, though the purpose is clear.

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