dataset_statscount, min, max, mean, median and sum of a numeric column of the Longtailo 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?
The description discloses how non-numeric rows are treated (excluded and counted) and mentions handling of commas/currency, giving some insight into edge cases. However, it does not mention potential errors, limitations, or whether the tool has any side effects, which leaves some behavioral aspects unspecified.
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, concise sentence that packs all necessary information without extraneous details. It is well-structured and front-loads the core purpose, making it easy to read and understand quickly.
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 description is self-contained for a simple tool with one parameter and no output schema. It names the dataset and lists the returned statistics, providing enough context for a user to know what to expect without needing additional documentation.
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 only parameter, 'column', is described as being a numeric column of the dataset, which adds meaningful context beyond the schema's generic 'string' type. This helps the user understand what kind of value to provide, though it could be more explicit about column naming conventions.
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 states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Longtailo dataset. The resource and operation are specific and unambiguous, making it easy to distinguish from other dataset tools.
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 explains that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which guides the user on data preprocessing expectations. While it does not explicitly compare to sibling tools, the purpose is clear enough for a user to infer when this tool is appropriate.
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 distinct query mode—schema, provenance, exact match, search, comparison, stats, and top-N—so there is little real ambiguity. The main possible confusion is between dataset_row and dataset_compare, since both filter by column values, but the descriptions clarify that compare is for ordered multi-value lookups while row handles single exact-value matches.
All tool names follow a consistent dataset_<noun> pattern with lowercase snake_case, making the family easy to recognize and predict. Though the names use nouns rather than verbs, the convention is uniform and clear.
Seven tools is a well-scoped set for querying and analyzing a single dataset. Each tool covers a distinct data-access need without redundancy or unnecessary bloat.
The surface covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, numeric stats, and top/bottom selection, which is comprehensive for typical dataset questions. A minor gap is the lack of categorical frequency counts or a way to retrieve all rows without a filter, but agents can work around those by combining existing tools.