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Rank rows by a numeric column

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The highest (or lowest) rows of the Cafmlane 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 carries the full behavioral burden. It does disclose that the tool ranks by a numeric column and can return either the highest or lowest rows. However, it does not mention default behaviors such as the default limit or default ordering (ascending), nor does it clarify whether the result includes ties or how the column is validated. For a read-oriented tool, these gaps are moderate.

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 concise and front-loads the core functionality: ranking rows by a numeric column. The example framing ('which is the most/least X') makes the intent understandable quickly. It earns its place without excessive detail, though it could benefit from a sentence about ordering semantics.

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?

Given the simple tool design (3 parameters, 1 required) and no output schema, the description covers the essential purpose and basic usage but leaves some gaps. It does not clarify whether 'column' must be numeric even though the description implies it, nor does it state the default ordering direction. The description is adequate for simple use but not complete for an agent to confidently invoke without additional inference.

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 33%, meaning only the 'ascending' parameter has a description. The description adds the concept of ranking (highest/lowest rows), which complements the schema's parameter list. However, it does not explain the meaning or constraints of 'column' or 'limit' beyond what the schema shows, so the added value is only moderate.

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 states a clear purpose: rank rows of the Cafmlane dataset by a numeric column and return the highest or lowest rows. It maps to the tool name well and conveys the core functionality. While it doesn't explicitly differentiate from sibling tools, the purpose is specific enough to be understood on its own.

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: when you need to find rows with the highest or lowest values in a numeric column ('which is the most/least X'). It does not explicitly state when to use this tool versus alternatives, and there are no named alternatives. The guidance is clear enough for a simple ranking tool but lacks explicit exclusions.

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
Disambiguation4/5

Each tool targets a distinct operation: schema, provenance, exact match, substring search, multi-value comparison, statistics, and ranking. There is some overlap between dataset_row and dataset_compare, but the descriptions clarify single-value vs multi-value use.

Naming Consistency5/5

All tools follow a consistent dataset_ noun pattern in snake_case. The naming clearly indicates the operation each tool performs.

Tool Count5/5

Seven tools is well-scoped for a dataset exploration server. Each tool earns its place by covering a distinct query mode without unnecessary redundancy.

Completeness4/5

The set covers schema inspection, provenance, exact filtering, substring search, comparison, summary statistics, and top/bottom ranking. Missing generic list-all or group-by aggregation, but the core analytical workflows are well covered.

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