dataset_compareThe rows of the The Water Hub 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 of behavioral disclosure. It does disclose two meaningful behaviors: rows are filtered by exact value membership and returned in the order supplied by the values array. It does not describe the output layout, pagination, error behavior, or whether the operation is read-only, though the title hints at the side-by-side presentation.
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 that front-loads the core behavior and ends with the concrete use case. There is no filler or repetition of schema details, and every clause contributes meaning.
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 compare tool, the description covers the essential filtering and ordering semantics, and the title supplies the output presentation style. With no output schema, a bit more detail about the returned structure would be ideal, but the description is nearly sufficient for an agent to call this tool correctly.
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 directly explains the meaning of both parameters: 'column' is the field to match and 'values' are the set of allowed matches, with array order controlling output order. This adds real semantic value beyond the raw schema, even though min/max item constraints remain in the schema.
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 a specific behavior: returning rows from The Water Hub dataset where a column matches any of the given values, preserving the given order. The title adds 'compare rows side by side,' which signals the comparison intent and helps distinguish it from generic row/search tools, though it does not explicitly name any sibling.
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 a clear use case and implies this tool is for comparing a small set of specific values. However, it does not mention when to use dataset_search, dataset_row, or dataset_stats instead, leaving alternative selection partly to inference.
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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Each tool targets a distinct operation on the dataset or enquiry. dataset_search, dataset_row, dataset_compare, and dataset_top have clear differences in matching logic and output. Enquiry tools are clearly separate. No two tools appear to do the same thing.
All dataset tools follow a dataset_* pattern, and enquiry tools follow enquiry_* except submit_enquiry which inverts the order. This is a minor deviation but the overall naming is predictable and clear.
10 tools is well-scoped for the domain, covering both data querying and enquiry submission without bloat.
The dataset query surface covers schema, metadata, row lookup, search, stats, and top/bottom. Enquiry covers description, fields, and submission. Minor gaps like a 'list all rows' or enquiry status check exist but are not critical.