Compare rows side by side
dataset_compareThe rows of the Reqnix 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 Reqnix 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 provided, the description carries the behavioral burden. It discloses two important behaviors: matching uses 'any of' the given values, and rows are returned in the order the values are given. However, it does not mention output format, empty-result behavior, or matching nuances like case sensitivity or exact-match semantics.
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 compact sentence that front-loads the core behavior and appends the intended use case. Every part earns its place, with no redundant or filler content.
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 simple two-parameter tool, the essential filter-and-order behavior is present. However, with no annotations and no output schema, the return shape and edge-case behavior remain unclear. The title's promise of 'side by side' comparison output is not reflected in the description body.
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 compensate. It does add meaning: 'column' is the field to match, and 'values' are the candidate values to match against, with row order following the order of values. This goes beyond the raw schema, though it could specify matching details more precisely.
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?
The description clearly states what the tool does: it retrieves rows from the Reqnix dataset where a specified column matches any of the given values, preserving the given order. The 'X vs Y' framing adds useful intent. It doesn't explicitly differentiate from sibling dataset tools, so it's not a 5.
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 description gives clear context by framing the tool for 'X vs Y' comparison-style questions. This helps an agent know when to select it. It does not provide exclusions or explicitly name alternative sibling tools, but the context is sufficient.
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 mode: schema, exact lookup, substring search, multi-value comparison, statistics, top/bottom ranking, and provenance. There is slight overlap between dataset_row, dataset_compare, and dataset_search, but their descriptions clarify exact, multi-value, and containing matches.
All tool names share the consistent dataset_* prefix and follow a clear pattern of dataset_ plus the operation or target concept. This makes the set predictable and easy to scan.
Seven tools is a well-scoped size for exploring a single dataset. Each tool covers a distinct query or metadata need without redundancy or bloat.
The tools cover the full dataset exploration lifecycle: schema discovery, provenance, exact row lookup, substring search, ordered comparison, numeric statistics, and ranking. There are no obvious dead ends for common questions about this dataset.