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Rank rows by a numeric column

dataset_top

The highest (or lowest) rows of the Stagenix 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

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It conveys a read-only ranking behavior and the highest/lowest intent, but it does not disclose edge-case behavior like ties, nulls, default sort order, or what the returned rows look like. The schema partially covers defaults, but the description itself is minimal.

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 one compact sentence with no wasted words, and the illustrative 'which is the most/least X' adds helpful context. It is slightly vague with 'Stagenix dataset,' but it remains appropriately concise.

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?

For a simple three-parameter read-only tool, the schema plus description are mostly workable, but the definition lacks explicit when-to-use guidance and does not describe output shape or default behavior. It is minimally viable but leaves gaps an agent would need to resolve.

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 description coverage is only 33%, but the description adds the important meaning that `column` must be numeric and clarifies the 'most/least' interpretation. However, `limit` is left to its name and schema bounds, so the description does not fully compensate for the missing parameter documentation.

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?

The description clearly identifies a specific action—returning the highest or lowest rows of the Stagenix dataset by a numeric column—which separates it from siblings like dataset_row or dataset_stats. It is specific about the resource and criterion, though it does not explicitly name alternatives.

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

Usage Guidelines2/5

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

No guidance is given about when to use dataset_top versus dataset_search, dataset_stats, dataset_row, or other siblings. There are no exclusions, prerequisites, or alternative conditions, so the agent must infer usage from the name alone.

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

A3.8/5.0
Disambiguation4/5

Most tools have clear, distinct purposes—schema, provenance, exact-match lookup, substring search, comparisons, statistics, and ranking. dataset_compare and dataset_row overlap somewhat since both filter by column values, but compare is explicitly for ordered multi-value 'X vs Y' queries while row is for a single exact match.

Naming Consistency5/5

All tools share a consistent dataset_ prefix and use clear, descriptive lowercase names. Even though some are nouns (columns, row, provenance) and others are verbs (compare, search, stats, top), the pattern is uniform and predictable.

Tool Count5/5

Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct mode of interaction—schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking—without unnecessary redundancy.

Completeness4/5

The tool surface covers the core read-only dataset exploration lifecycle well: understand schema, get provenance, retrieve rows, search, compare, compute statistics, and rank. A minor gap is the absence of a tool for fetching distinct values or arbitrary aggregations, but this is not a critical dead end for the stated purpose.

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