dataset_statscount, min, max, mean, median and sum of a numeric column of the Retainvia 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, the description carries the behavioral disclosure burden. It goes beyond a simple summary by revealing that grouping commas and currency are parsed, and that non-numeric rows are excluded and counted. This is valuable transparency for a data-processing tool, though it omits details like error handling or empty-column 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?
A single, efficient sentence packs the core purpose, target dataset, computed statistics, and a key parsing behavior. There is no fluff or repetition; every element contributes to understanding the 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?
For a tool with one parameter, no output schema, and no annotations, the description covers purpose, applicable data types, and an important data-cleaning characteristic. It is reasonably complete for the low complexity, though return format and error conditions are not described.
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 does clarify that the column must be numeric and mentions formatting handling, which adds meaning beyond the schema's bare 'string' type. However, it doesn't explain how to reference the column, whether exact names are required, or any edge cases beyond non-numeric rows.
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 enumerates the statistics computed (count, min, max, mean, median, sum) and names the target resource (Retainvia dataset). It is distinguishable from siblings like dataset_search or dataset_top, though it lacks an explicit verb such as 'computes' or '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 usage for numeric columns and signals that currency/grouping formats are handled, but it does not explicitly state when to prefer this tool over alternatives such as dataset_top or dataset_compare. No exclusions or alternative routing are provided.
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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Most tools have distinct purposes, but dataset_row, dataset_search, and dataset_compare all retrieve rows via value matching, creating possible misselection. The descriptions help clarify exact vs. contains vs. list comparison, but boundaries are not crisp.
All tools share a consistent dataset_ prefix and snake_case formatting, making the group recognizable. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for exploring and querying a single dataset. Each tool covers a distinct need without bloat or redundancy at the set level.
The toolset covers schema inspection, provenance, stats, exact lookup, substring search, comparisons, and top/bottom ordering, which forms a solid read-only exploration surface. Minor gaps exist, such as multi-condition filtering or grouped aggregations, but agents can likely work around them.