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Rank rows by a numeric column

dataset_top

The highest (or lowest) rows of the Tieoutly dataset by a numeric column — "which is the most/least X".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
columnYes
ascendingNotrue for the lowest first; default highest first

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of explaining behavior. It states that rows are returned in highest/lowest order by a numeric column, but it does not disclose the default limit, whether the result is a sorted subset, how ties are handled, or that this is a read-only operation. These are meaningful gaps for a tool with no annotation safety hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that conveys the core ranking concept and a practical interpretation. It is front-loaded with the key behavior and contains no filler, though it is slightly informal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, but with no output schema and only 33% schema coverage, the description leaves important details unstated, especially the limit parameter and return shape. It is adequate for an experienced agent to infer the main purpose, but not complete enough to guarantee correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%, and the description compensates only partially. It adds that 'column' must be numeric and ties 'ascending' to highest/lowest ordering, but 'limit' is entirely undocumented in both the schema and the description. The description does not clarify default values or how the limit interacts with ranking.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Title and description together clearly identify the tool as ranking rows by a numeric column (top or bottom). The 'which is the most/least X' phrasing gives a concrete use case and distinguishes it from search, stats, and row-fetching siblings. However, it does not explicitly name any sibling or contrast its scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: use this when you want the most/least values of a numeric column. There is no explicit statement about when not to use it or which sibling alternative to choose, so the guidance is only implied rather than stated.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: schema introspection, exact-match lookup, substring search, multi-value comparison, statistical aggregation, ranking, and provenance metadata. No two tools overlap in functionality, making misselection unlikely.

Naming Consistency5/5

All tools follow a consistent 'dataset_<descriptor>' pattern, where the descriptor is a noun or verb indicating the operation (columns, compare, provenance, row, search, stats, top). This uniformity aids predictability and discoverability.

Tool Count5/5

Seven tools is well-scoped for a dataset-querying server. Each tool covers a distinct query type or metadata aspect, and none are redundant or unnecessary.

Completeness5/5

The tool surface covers the primary ways to interact with the dataset: retrieving schema, accessing rows via exact match, substring search, multi-value comparison, computing statistics, finding top/bottom values, and citing provenance. This covers the full lifecycle of typical dataset questions without obvious gaps.

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