dataset_statscount, min, max, mean, median and sum of a numeric column of the Disclovo 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?
No annotations are provided, so the description carries the burden. It discloses meaningful parsing behavior: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This is useful beyond the schema, though it stops short of describing behavior for empty or all-non-numeric columns.
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 sentence states the core metrics first and adds the parsing caveat in a tight parenthetical. No redundant or filler content.
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 low-complexity, one-parameter stats tool, the description is largely complete: it lists the computed metrics and data-cleaning behavior. However, the meaning of 'count' after non-numeric rows are 'excluded and counted' is slightly ambiguous, and behavior for an empty or entirely non-numeric column is not stated.
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% and the column parameter only has a minLength constraint. The description compensates by specifying that the column must be numeric and that values with grouping commas or currency are handled. It does not specify exact column-name matching rules, but for one parameter this is adequate.
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 lists the exact statistics returned (count, min, max, mean, median, sum) and identifies the resource as a numeric column of the Disclovo dataset. This goes beyond the title and distinguishes it from sibling tools like dataset_columns or dataset_row, though it does not use an explicit verb such as '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 the tool is for computing summary statistics on a numeric column, and the edge-case note clarifies acceptable input values. It does not explicitly say when to prefer this over siblings like dataset_top or dataset_search, nor does it state exclusions.
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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Each tool targets a clearly distinct operation: schema introspection, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. Despite some overlap among row, compare, and search, the descriptions make the boundaries obvious.
All tools follow the same dataset_ prefix and use short, readable operation names. The naming is uniform and predictable, making it easy for an agent to infer the purpose of each tool.
Seven tools is a well-scoped size for a dataset Q&A server. Each tool provides a distinct capability without unnecessary duplication, and the count is appropriate for the domain.
The tool set covers the core dataset workflow: schema discovery, provenance, row retrieval, search, comparison, statistics, and ranking. Minor gaps exist such as multi-column filtering or distinct-value extraction, but these are not major blockers for typical questions.