dataset_statscount, min, max, mean, median and sum of a numeric column of the Reconzy 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 full burden of behavioral disclosure. It reveals that grouping commas and currency are handled, and non-numeric rows are excluded and counted, which is useful preprocessing context. However, it does not mention error cases or output format, which would be helpful but not critical for a simple statistics tool.
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 sentence that front-loads the statistics list and then adds concise preprocessing details. There is no unnecessary wording, making it appropriately sized and easy to parse.
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?
The tool is simple with one parameter and no output schema. The description covers the purpose, numeric column requirement, and data handling. It does not specify the return format, but for a statistics tool, the output is largely self-evident. Given the lack of annotations and output schema, it is reasonably 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?
The schema provides no description for the 'column' parameter (0% coverage), so the description must clarify it. The description indicates the column must be numeric and explains how non-numeric rows are handled, adding meaning beyond the bare schema. It does not explicitly restate that 'column' is the parameter name, but it is clearly implied.
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 explicitly states the tool computes count, min, max, mean, median, and sum for a numeric column of the Reconzy dataset. This is a specific verb+resource that clearly distinguishes it from siblings like dataset_columns or dataset_top, which handle different dataset aspects.
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 when summary statistics on a numeric column are needed, but it does not explicitly mention when not to use it or name alternative tools. There is no exclusion or routing to siblings, so the guidance is only implied.
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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Tools are mostly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping row-filtering purposes. Descriptions clarify exact match vs. contains vs. any-of, so confusion is unlikely but possible.
All tools share the dataset_ prefix, but the suffix pattern is inconsistent: some are nouns (columns, provenance, row, stats, top) and some are verbs (compare, search). A more uniform verb_noun or noun_only convention would improve predictability.
Seven tools is a well-scoped set for dataset exploration, covering schema, provenance, lookup, search, comparison, statistics, and top-values queries without redundancy or bloat.
The toolset covers the main dataset exploration operations well, but it lacks explicit distinct-value or grouped-aggregation tools, which could be useful for fuller ad-hoc analysis. It is not severely incomplete.