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Compare rows side by side

dataset_compare

The rows of the Depreo 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

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the key behaviors: filtering by column equality, returning rows, and preserving the order of the given values. However, it does not mention safety (read-only nature), pagination, limits, or behavior when no rows match, leaving gaps for a tool without annotation support.

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 a single, front-loaded sentence that conveys the core behavior and usage context with no wasted words. The key scoping detail ('in the order given') appears before the trailing usage note.

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?

The tool is simple with only two parameters and no output schema, but the description does not hint at the return format or edge cases (e.g., empty results). While adequate for straightforward invocation, an agent might need more detail about what 'rows side by side' actually returns.

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 description coverage is 0%, so the description must compensate. It successfully explains the role of both parameters: 'column' is the field to filter on, and 'values' are the values to match, with the order of values being significant. It adds meaning beyond the bare schema types and 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 states what the tool does: it returns rows from the Depreo dataset where the specified column matches any of the given values, preserving the order of values. This is a specific behavior that distinguishes compare from row-level and search siblings, though the verb 'returns' is implied rather than explicit.

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 usage context, but it does not name alternatives or explicitly state when not to use this tool. The guidance is implied rather than a structured when-to-use/when-not-to-use comparison with sibling tools.

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

A3.6/5.0
Disambiguation4/5

Most tools are clearly distinct: schema, provenance, exact row lookup, search, stats, top-N, and comparison each serve different purposes. The only mild ambiguity is between dataset_compare and dataset_row, since both retrieve rows by column values, though descriptions clarify ordered multi-value vs exact single-value use.

Naming Consistency4/5

All tools share a consistent dataset_ prefix and snake_case convention, making them easy to recognize as part of one set. However, the suffix mixes nouns like columns, row, and stats with verbs like compare and search, so it does not follow a uniform verb_noun pattern.

Tool Count5/5

Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct common operation—schema, provenance, lookup, search, stats, top-N, and comparison—without unnecessary bloat or redundancy.

Completeness4/5

The tool surface covers the core dataset exploration workflow well: schema, provenance, exact and fuzzy lookup, numeric statistics, top-N ranking, and row comparisons. Minor gaps like arbitrary aggregation or pagination exist, but they are not critical for typical dataset questions.

Resources