dataset_topThe highest (or lowest) rows of the Take-Home Compass 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 carries the full burden. It implies a read-only operation (returning rows) and clarifies the numeric column constraint, but does not explicitly mention side effects, permissions, or error behavior. The description is honest but minimal.
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 concise and front-loaded: 'The highest (or lowest) rows...' immediately conveys the core function. The parenthetical and quote add clarity without redundancy. Every word earns its place.
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 tool, the description provides a basic understanding of what it does (ranking rows), but lacks details on output structure (what fields are returned), the effect of 'limit' (e.g., default behavior), and edge cases. The absence of an output schema increases the need for such context, which is not fully supplied.
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 description adds meaning to 'column' (numeric) and 'ascending' (highest/lowest order), but does not clarify the 'limit' parameter at all. Since only one parameter has a schema description, the tool description covers part of the parameter semantics but leaves a gap.
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 returns rows ranked by a numeric column, either highest or lowest, and specifies the 'most/least X' query intent. This distinguishes it from sibling tools like dataset_search (searching) and dataset_stats (aggregates).
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 for ranking queries via 'which is the most/least X' but does not explicitly state when to use this tool versus alternatives like dataset_stats or dataset_search. No explicit guidance on when not to use it.
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 has a clearly distinct purpose: schema, provenance, exact-row lookup, substring search, multi-value comparison, aggregate stats, top/bottom ranking, and enquiry lifecycle steps. Even the three query tools (dataset_row, dataset_compare, dataset_search) are semantically separate and described with enough precision to avoid misselection.
All tools follow a consistent snake_case pattern with a domain prefix: dataset_* for the data exploration tools and enquiry_* for the form workflow. The suffix is sometimes a noun (columns, provenance, fields) and sometimes a verb (search, compare, submit), but the uniform prefix and predictable structure make the set easy to navigate.
Ten tools is a well-scoped size for a server covering two related areas: dataset analysis and enquiry submission. Each tool earns its place; there is no obvious redundancy or bloat, and the split of seven dataset tools and three enquiry tools matches the apparent purpose.
The dataset tools cover schema discovery, provenance, filtering, searching, comparison, statistics, and ordering—a solid analytical surface. The only notable gap is a way to retrieve all rows at once without a filter, though that may be intentionally omitted since most queries are targeted. The enquiry tools form a complete describe-fields-submit flow.