dataset_statscount, min, max, mean, median and sum of a numeric column of the Extinvo dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the behavioral disclosure burden. It usefully discloses that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. It does not mention error cases or output structure, but the read-only nature of statistics is clear from the content.
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 dense sentence with no filler. It front-loads the computed statistics and adds relevant handling details about commas, currency, and non-numeric rows without redundancy.
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 one-parameter tool, the description covers the core purpose, the target dataset, the list of metrics, and important input quirks. It lacks explicit output format and behavior when the column is missing or entirely non-numeric, but these are minor gaps given the tool's simplicity.
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 clarify the parameter. It does indicate that 'column' refers to a numeric column of the Extinvo dataset, but it does not explain how column names are specified or how to discover valid columns. The single parameter is simple, but the lack of format details leaves some ambiguity.
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 specifies the tool's function: computing count, min, max, mean, median and sum for a numeric column. It names the exact dataset (Extinvo) and distinguishes itself from siblings like dataset_search or dataset_top by focusing on summary statistics for a single 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 usage context is implied rather than explicit: an agent can infer this tool is for summarizing a numeric column. However, the description does not state when to use this tool over siblings such as dataset_top or dataset_columns, nor does it provide exclusion criteria or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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The tools are mostly distinct: columns, provenance, stats, and top each have a single clear role, while dataset_row, dataset_search, and dataset_compare all return rows but differ by exact match, substring containment, and ordered value comparison. The descriptions explain these differences clearly, so misselection is unlikely but still possible.
Every tool follows the same dataset_<operation> pattern with a clear noun or verb suffix. The naming is predictable and the row-returning tools use distinct names (row, search, compare) that match their behavior.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a meaningful operation and none feel redundant or superfluous.
The set provides schema, provenance, exact lookup, free-text search, compare, stats, and top/bottom ranking, which covers the main ways an agent would query this dataset. No obvious dead-end or missing core operation is apparent.