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Rank rows by a numeric column

dataset_top

The highest (or lowest) rows of the Kbasevo 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

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It does explain the ranking direction (highest or lowest) and the numeric-column requirement, but it does not mention what happens with invalid or missing values, the default limit behavior, or the shape of the returned rows.

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 sentence with no filler and the useful paraphrase 'which is the most/least X' is included. It is compact and front-loaded with the core behavior, though the dash construction is slightly awkward.

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 ranking tool, the description covers the essential behavior and invocation intent. But with no output schema and no annotations, it would benefit from stating the return format and any default limit behavior, so the context is adequate but not fully specified.

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%, so the description needs to compensate. It adds useful meaning for the required 'column' parameter by specifying that it must be numeric, and it reinforces the meaning of 'ascending' via highest/lowest wording. However, it says nothing about the 'limit' parameter, leaving part of the parameter meaning to inference from the property name and schema constraints.

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 states that the tool returns the highest or lowest rows of the Kbasevo dataset by a numeric column, and the quoted 'which is the most/least X' question captures the intended use. It is distinct from siblings like dataset_stats or dataset_row, though it does not explicitly name any alternative.

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 when to use the tool: when asking a 'most/least X' ranking question about a numeric column. However, it gives no explicit guidance about when not to use it or which sibling tool to prefer instead, such as dataset_stats for aggregate summaries or dataset_search for filtered access.

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

A3.7/5.0
Disambiguation4/5

The tools split cleanly into metadata (columns, provenance), retrieval (row, search, compare), and aggregation (stats, top). dataset_row and dataset_compare overlap somewhat since both filter by column values, but the multi-value ordered comparison purpose is distinct enough.

Naming Consistency4/5

All tools share the dataset_ prefix and snake_case convention, making the family recognizable. However, the second half mixes noun-like names (columns, row, stats, top) with verb-like names (compare, search), so the pattern is consistent but not uniformly verb_noun.

Tool Count5/5

Seven tools is a well-scoped set for exploring a single dataset: schema, provenance, exact lookup, substring search, comparisons, numeric stats, and extremes. No tool feels redundant, and the count is appropriate for the server's purpose.

Completeness4/5

The surface covers the common dataset questions: schema, attribution, exact matching, fuzzy search, comparative queries, numeric summaries, and ranking. It lacks advanced multi-condition filtering or full-dump pagination, but those are not clearly required for this read-only dataset browser.

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