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Compare rows side by side

dataset_compare

The rows of the Upkeepvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnYes
valuesYes

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It does disclose two key traits: rows are ordered according to the input values order, and matching is an OR over the given values. However, it omits output format, no-match behavior, and error handling, leaving meaningful gaps.

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 efficient sentence that front-loads the core filtering and ordering behavior and ends with the use case. It is appropriately concise for a simple two-parameter tool, though the 'side by side' title and the actual return behavior are slightly misaligned.

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 tool's simplicity, no output schema, and no annotations, the description adequately explains what rows are returned, the ordering rule, and the intended use case. However, it leaves the result format and no-match behavior unspecified, which an agent may need to know for reliable invocation.

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

Parameters4/5

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

Schema coverage is 0%, but the description compensates by explaining that `column` is the attribute to filter on and `values` are the matching values whose order determines the row order. This adds essential meaning beyond the bare schema, though it does not restate constraints already present in the schema.

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 description clearly states the tool returns rows from the Upkeepvo dataset filtered by a column matching any of the given values, preserving the given order. This differentiates it from siblings like dataset_row (single row) and dataset_search (general search), and the 'X vs Y' phrase signals its comparative intent.

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 phrase 'for "X vs Y" questions' provides a clear use case, but the description does not explicitly state when not to use this tool or when to prefer an alternative such as dataset_search or dataset_row. The intended usage is implied rather than fully specified.

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

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: schema discovery, comparison, provenance, exact row lookup, substring search, stats, and ranking. No two tools overlap in functionality, making misselection unlikely.

Naming Consistency5/5

All tools follow the consistent pattern 'dataset_' followed by a lowercase noun or verb (columns, compare, provenance, row, search, stats, top). The naming is uniform and predictable.

Tool Count5/5

With 7 tools, the set is well-scoped for a dataset querying server. Each tool covers a distinct query need without redundancy or bloat.

Completeness5/5

The tools provide comprehensive read-only access to the dataset: schema, row retrieval, search, statistics, ranking, comparisons, and provenance. No obvious gaps for typical analysis workflows.

Resources