dataset_compareThe rows of the HomeCover HQ 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 burden of behavioral disclosure. It explains the filtering behavior and ordering, but it does not describe the output shape, whether rows are truly compared side-by-side, or any limitations beyond the schema constraints.
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 sentence with no superfluous content, and the filtering/ordering behavior is stated before the usage note. It is concise and adequately front-loaded, though slightly awkward in phrasing.
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 description gives enough to understand the core purpose and basic invocation. However, with no annotations and no output schema, it does not disclose the return format, valid column sources, or any edge-case behavior, leaving some gaps.
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 explains the relationship between the two parameters: 'column' is the dataset column being filtered, and 'values' are the values to match. Since schema coverage is 0%, this adds useful meaning, though it does not fully define parameter formats or constraints beyond what is inferable.
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 that the tool returns rows from the HomeCover HQ dataset filtered by a column matching any of the given values, preserving the given order. This differentiates it from dataset_search and dataset_row, though it lacks an explicit verb like 'returns' or 'selects'.
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' provides clear context on when to use this tool. It does not explicitly mention when not to use it or name alternative sibling tools, but the intended use case is reasonably clear.
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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Most tools have clearly distinct purposes: schema, provenance, exact row lookup, substring search, comparison, stats, top values, and enquiry steps are all separate. The only mild ambiguity is between dataset_row, dataset_search, and dataset_compare, but their descriptions clarify exact matching, substring matching, and ordered value comparison respectively.
The dataset_* prefix and enquiry_* prefix create a clear grouping. Within each group the pattern is mostly consistent, though some names are noun-based (dataset_columns, dataset_provenance) while others are verb-based (dataset_search, dataset_compare), and submit_enquiry reverses the prefix order.
Ten tools is a well-scoped set for this domain: seven query tools cover the dataset surface and three cover the enquiry flow. Each tool has a distinct job and none feel redundant.
The dataset side covers schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking, which covers the full range of likely questions. The enquiry side handles explaining the process, listing fields, and submitting with a two-step confirmation, leaving no obvious dead ends.