dataset_compareThe rows of the MultiplesBook 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, so the description carries the full burden. It does disclose meaningful behavior: row selection by column-value membership, OR semantics via 'any of', and output order according to the given values. It does not cover exact-match/case sensitivity, no-match behavior, or confirm the operation is read-only, which a fuller description could add.
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 entire description is one compact sentence with no filler. The selection rule is front-loaded and the 'for X vs Y questions' usage context is added with minimal cost.
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 two-parameter tool with no output schema and no annotations, the description defines the selection and ordering behavior plus a usage context. It still omits the return format, behavior when no rows match, and guidance among sibling tools, so it is minimally adequate rather than complete.
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 relate the two parameters: 'column' is the field being matched and 'values' are the candidate values, with order emphasized. However, it does not explain the string/array types or the min 2 / max 10 constraint beyond what the schema already shows.
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 states that the tool returns the rows of the MultiplesBook dataset for a specific column matching any of the provided values, in the order given. The title and the 'X vs Y' phrasing clarify it is for side-by-side comparison, but it does not explicitly name how it differs from siblings like dataset_row or dataset_search.
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?
'For X vs Y questions' gives a clear intended use context, and the ordering note tells the caller that the order of the values matters. It does not explicitly mention alternatives or state when not to use it, so it does not reach the top score.
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.
Most tools target distinct query types: schema, provenance, exact row lookup, search, comparisons, stats, and top/bottom. However, dataset_row, dataset_search, and dataset_compare have some functional overlap around filtering rows, which could lead to selection mistakes.
All tools follow a consistent dataset_<operation> pattern with clear nouns describing the operation. This makes the tool set predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct common question type without unnecessary proliferation.
The surface covers the main ways users would query the dataset: schema, exact matches, substring search, comparisons, stats, and top/bottom. A minor gap is the lack of a simple 'get all rows' or pagination tool, but that is not required for the apparent Q&A purpose.