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Compare rows side by side

dataset_compare

The rows of the Sowbird 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

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of explaining behavior. It implies a read-only retrieval by describing rows being returned, but it does not explicitly state that there are no side effects or mutations.

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 concise and front-loaded, with the title and first clause immediately conveying the operation. The extra phrase about 'X vs Y' questions adds relevant context without unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides enough context for the agent to understand the resource, the filtering mechanism, and the ordering behavior. It lacks an explicit output schema, but for a row-returning tool, the description is sufficiently complete for correct 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?

The description explains both parameters: 'column' is the field to match, and 'values' are the allowed values to filter rows. It also clarifies that rows are returned in the order of the given values, adding meaning beyond the raw schema.

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 indicates that the tool returns rows from the Sowbird dataset matching any of the given values in a specified column, in the order provided. The title 'Compare rows side by side' reinforces the intended comparison use case, and the description differentiates it from single-row or search tools.

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

Usage Guidelines4/5

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

The description explicitly states that the tool is for 'X vs Y' questions, which tells the agent when to use it for comparisons. It does not explicitly mention sibling alternatives, but the use-case hint is sufficient guidance.

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

Each tool targets a distinct query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The descriptions clearly differentiate row/compare/search, though row and compare have some conceptual overlap.

Naming Consistency5/5

All tool names follow the same `dataset_` prefix followed by a noun (columns, compare, provenance, row, search, stats, top), creating a predictable and consistent naming pattern.

Tool Count5/5

Seven tools is well-scoped for a read-only dataset exploration server. Each tool serves a unique purpose with no redundancy, covering schema, metadata, lookup, search, comparison, statistics, and ranking.

Completeness4/5

The set covers schema, provenance, exact and substring search, comparisons, summary stats, and extremes, which handles most dataset Q&A needs. Minor gaps like group-by aggregation or pagination are not critical for the apparent purpose.

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