dataset_compareThe rows of the HardscapeDesk 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?
No annotations are provided, and the description does not mention whether the operation is read-only, has side effects, requires authentication, or is subject to rate limits. It only describes the data returned, not the operational behavior.
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, concise sentence that communicates the essential behavior without unnecessary filler. It is easy to read and front-loads the key information.
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?
While the description conveys the basic filtering and ordering, it does not specify whether each value corresponds to exactly one row (implying a 1:1 mapping for side-by-side comparison) or whether duplicate matches are included. It also omits details about the output format, which could be important for correct usage.
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?
The description references 'column' and 'values' and explains that rows are matched when the column equals any of the given values. However, it does not clarify the exact matching semantics (e.g., exact string match) or whether multiple rows per value are returned, leaving some meaning to be inferred from the schema constraints.
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 the tool returns rows from the HardscapeDesk dataset filtered by a column matching any of the given values, with order preserved for side-by-side comparison. This is specific and distinguishes it from generic row retrieval.
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?
It provides a use case ('for X vs Y questions') that hints at when to use it, but does not explicitly contrast with alternatives like dataset_search or dataset_row, leaving some ambiguity about when one should be preferred over the other.
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 clearly distinct operation: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. No two tools appear to serve the same purpose.
All tool names follow the same 'dataset_' prefix pattern followed by a simple noun or operation name (columns, compare, provenance, row, search, stats, top). This is highly consistent and predictable.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct need without redundancy or bloat.
The surface covers schema discovery, data retrieval, search, comparison, statistics, extremes, and provenance. Minor gaps exist, such as no explicit way to retrieve all rows or generate distinct-value lists, but core query workflows are well supported.