Compare rows side by side
dataset_compareThe rows of the Walkvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
dataset_compareThe rows of the Walkvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It transparently states the selection rule and ordering behavior, but says nothing about output format, row limits, read-only nature, or how the 'side-by-side' comparison actually materializes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently communicates the core selection behavior. The trailing em-dash clause is compact but somewhat vague, so it does not fully earn a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema and no annotations, the description conveys the core selection rule and intended use case. However, it omits return/presentation details—the 'side-by-side' format is never explained—and does not describe what the output looks like, leaving the agent with some uncertainty before invoking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain the parameters. It clarifies that column is the field to compare and values are the candidate values to match in given order, which partially compensates. Yet it does not specify exact-match semantics, valid column names, or behavior for missing values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific behavior: return rows whose column matches any of the given values, in the order given, and ties it to 'X vs Y' questions. It is distinguishable from siblings like dataset_stats or dataset_search in intent, but the title's 'Compare rows side by side' suggests a presentation format that the description does not clearly deliver.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for X vs Y questions' implies when the tool is appropriate, giving useful context. However, it does not explicitly name alternatives among the six sibling tools, nor state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct query type (schema, exact match, substring search, multi-value comparison, stats, top/bottom, provenance), but dataset_row and dataset_compare overlap for single-value exact matches, and dataset_columns' provenance banner overlaps somewhat with dataset_provenance.
All tools share the dataset_ prefix and snake_case, but suffixes mix nouns (columns, provenance, row, stats) with verbs (compare, search) and an adjective (top), so the pattern isn't as uniform as a strict verb_noun convention.
Seven tools is a well-scoped set for read-only dataset exploration; each operation (schema, lookup, search, compare, stats, top, provenance) earns its place.
Covers the core dataset workflows: schema, exact and fuzzy lookup, comparisons, numeric summaries, and attribution. Minor gaps like grouped aggregations or multi-condition filters are absent but not essential for the stated purpose.