dataset_compareThe rows of the TimeCardBook dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | 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 behavioral burden. It does disclose useful traits: rows are ordered by the given value order and matching is by 'any of' the values. It does not mention output format, case sensitivity, exact-match semantics, or behavior when no rows match.
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 with no filler, and the core filtering behavior is front-loaded. The syntax is slightly awkward (a noun phrase rather than an explicit imperative), but it remains compact and efficient.
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 simple two-parameter retrieval tool with no output schema, the description states what is returned (rows selected by column values) and the ordering, which is the key context. It does not detail row shape or edge cases, but those are likely discoverable via sibling tools like dataset_columns.
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 is the only source of parameter meaning. It explains that 'column' is the field to match against and 'values' are the accepted values, and that their order drives the output row order—adding real semantics beyond the raw schema.
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 identifies the TimeCardBook dataset and the row-selection behavior (column matching any of the given values, in given order), and the title supplies the 'compare' verb. It distinguishes this tool from siblings by specifying value-based row selection for comparison, though it lacks a direct main verb like '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 phrase 'for X vs Y questions' gives a clear use case for side-by-side comparison, and the ordering detail implies the intended comparison workflow. However, it does not explicitly name alternatives or state when not to use this tool.
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.