dataset_topThe highest (or lowest) rows of the Lessonvo 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 provided, the description carries the full burden and does state the core behavior: ranking rows by a numeric column in either direction. It does not disclose tie-breaking, null handling, default limit behavior, or whether the operation is purely read-only, which are meaningful gaps for a no-annotation tool.
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 front-loaded sentence that communicates purpose, numeric-column requirement, and direction with no filler. The clarifying 'most/least X' example 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 top-N ranking tool, the description is minimally viable: it covers what the tool returns and which column type is required. Gaps remain around the meaning of an omitted 'limit', output shape (since there is no output schema), and edge-case behavior such as ties or missing values.
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 only 33%, so the description must compensate. It adds important semantics by specifying that 'column' must be numeric and indicating the high/low direction. However, it does not explain the meaning or default behavior of 'limit' beyond what the parameter name and constraints imply.
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 operation: returning the highest or lowest rows of a dataset by a numeric column. It is more specific than a plain 'rank' statement and gives a natural-language framing ('which is the most/least X'), though it does not explicitly contrast itself with sibling 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 implies when to use the tool through the 'most/least X' phrasing, which signals top-N queries. However, it does not explicitly state when to prefer this over siblings like dataset_stats or dataset_search, leaving exclusion guidance to inference.
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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Most tools target clearly distinct operations: schema, provenance, exact match, multi-value match, substring search, stats, and top-N. The only potential confusion is between dataset_row and dataset_compare, which both fetch matching rows but differ in single vs. multiple values — the descriptions make this distinction reasonably clear.
All tools share the consistent 'dataset_' prefix in snake_case, which is good. However, the suffix mixes nouns (columns, provenance, row, stats) with verbs (compare, search), and 'dataset_top' is cryptic while 'dataset_row' is singular despite returning rows.
Seven tools is well-scoped for a single-dataset query server. Each tool earns its place covering a distinct query type: schema, attribution, exact lookup, set membership, substring search, aggregation, and ranking.
The surface covers schema, provenance, exact/partial lookup, comparison, stats, and top-N queries well. Obvious gaps include no way to page through or list all rows, no multi-condition (AND) filtering, and no group-by counts — limitations that may force agents to work around when answering comparison questions.