dataset_topThe highest (or lowest) rows of the TimeCardBook dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavior. It explains that rows are returned ordered by highest or lowest values, but does not mention default limit behavior, tie handling, or whether the full row is returned. The schema only documents the ascending parameter, leaving these details 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, immediately front-loading the core action and supporting phrase. No unnecessary words or 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?
The tool is simple and has no output schema; the description adequately conveys that rows are returned for the top/bottom by a numeric column. It lacks explicit mention of default limit and output shape, but these are minor gaps given the tool's straightforward nature.
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 low (only 33% for ascending). The description compensates by indicating that 'column' must be numeric and implying that ascending relates to lowest ordering, but it adds no meaning for the 'limit' parameter, whose semantics remain undocumented.
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 ranks rows by a numeric column and returns the highest or lowest rows, directly addressing 'most/least X' questions. The title reinforces this by naming the action 'Rank rows by a 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 description includes a natural-language use case ('which is the most/least X') that helps an agent know when to invoke this tool. It does not explicitly contrast with sibling tools, but the ranking-specific language is sufficient for selecting it over search/statistics tools in most cases.
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 addresses a distinct query pattern: schema discovery, provenance, exact-match lookup, substring search, ordered multi-value comparison, column statistics, and top/bottom row ranking. The only close pair is dataset_row and dataset_compare, but their descriptions clearly separate exact single-value equality from ordered value-list comparison.
All seven tools use the same dataset_ prefix and snake_case convention, producing a predictable and scannable set. Although suffixes mix nouns (columns, stats) and verbs (compare, search), the consistent prefix and clear semantic labels make naming highly regular.
Seven tools is well-scoped for read-only interrogation of a single dataset, covering metadata, lookup, search, comparison, statistics, and extreme values without redundancy. The count is comfortably in the ideal range for this purpose.
The surface covers the main dataset operations: schema, provenance, exact and fuzzy retrieval, comparisons, aggregates, and ranking. A minor gap is the lack of a way to retrieve all rows or list unique categorical values, but most realistic questions can be answered with the provided patterns.