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Rank rows by a numeric column

dataset_top

The highest (or lowest) rows of the HardscapeDesk 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

A4/5.0
Behavior3/5

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

The description discloses the core ordering behavior and the ascending parameter is documented in the schema. However, with no annotations, it does not fully describe returned row shape, default limit behavior, tie handling, or whether the operation is strictly read-only.

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

Conciseness5/5

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

The description is one concise sentence that front-loads the key behavior and includes a useful natural-language query template. No unnecessary words or repetition.

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

Completeness4/5

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

For a simple ranking tool, the description plus schema is largely sufficient to understand what the tool does and how to call it. Minor gaps like default limit and return format prevent a perfect score, but the overall context is adequate.

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

Parameters3/5

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

Schema coverage is low (only ascending has a description), but the description adds the important constraint that the column must be numeric. It does not clarify the default limit or explain how limit interacts with ranking, leaving some parameter semantics incomplete.

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

Purpose5/5

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

The title and description clearly state the tool ranks dataset rows by a numeric column and returns the highest or lowest values. The phrase 'which is the most/least X' makes the purpose concrete and distinguishes it from sibling tools like dataset_search and dataset_stats.

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

Usage Guidelines4/5

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

The description gives a clear use case: retrieving top or bottom rows by a numeric column. It does not explicitly name alternatives or exclusions, but the 'most/least X' framing provides enough context for when this tool is appropriate.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a clearly distinct operation: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. No two tools appear to serve the same purpose.

Naming Consistency5/5

All tool names follow the same 'dataset_' prefix pattern followed by a simple noun or operation name (columns, compare, provenance, row, search, stats, top). This is highly consistent and predictable.

Tool Count5/5

Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct need without redundancy or bloat.

Completeness4/5

The surface covers schema discovery, data retrieval, search, comparison, statistics, extremes, and provenance. Minor gaps exist, such as no explicit way to retrieve all rows or generate distinct-value lists, but core query workflows are well supported.

Resources