dataset_compareThe rows of the Take-Home Compass 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?
The description states the main behavior: selecting rows whose column value matches any of the provided values, preserving the given order. However, with no annotations, it does not disclose whether the operation is read-only, what the output format is, or how results are paginated or limited. The core behavior is transparent, but edge-case behavior is not covered.
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, tightly worded sentence with no redundant information. It effectively conveys the core behavior and the intended use case in a compact form. The structure is clean and easy to parse.
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?
The description provides enough context for a simple filtering operation and names the target dataset. However, it lacks details about output structure, parameter semantics, and relationship to sibling tools. For a tool with no output schema and no annotations, this leaves some gaps for the caller.
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 refers to 'column' and 'given values' but does not explicitly map them to the 'column' and 'values' parameters. It implies that 'column' is the dataset column to filter on and 'values' are the accepted values, but it does not explain constraints like minimum two values or the meaning of the order. Since the schema has no parameter descriptions, the description only partially compensates.
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 identifies the tool's purpose: returning rows from the Take-Home Compass dataset where a specified column matches any of the given values, in the provided order. The title 'Compare rows side by side' and the phrase 'for X vs Y questions' add helpful intent context. It could be more explicit about the relationship to sibling tools, but the core purpose is unambiguous.
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 minimal guidance on when to use this tool. It mentions 'for X vs Y questions' but does not contrast it with sibling tools like dataset_search, dataset_row, or dataset_top, nor does it explain when this tool should be preferred. Users are left to infer the appropriate usage context from the phrase alone.
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 has a clearly distinct purpose: schema, provenance, exact-row lookup, substring search, multi-value comparison, aggregate stats, top/bottom ranking, and enquiry lifecycle steps. Even the three query tools (dataset_row, dataset_compare, dataset_search) are semantically separate and described with enough precision to avoid misselection.
All tools follow a consistent snake_case pattern with a domain prefix: dataset_* for the data exploration tools and enquiry_* for the form workflow. The suffix is sometimes a noun (columns, provenance, fields) and sometimes a verb (search, compare, submit), but the uniform prefix and predictable structure make the set easy to navigate.
Ten tools is a well-scoped size for a server covering two related areas: dataset analysis and enquiry submission. Each tool earns its place; there is no obvious redundancy or bloat, and the split of seven dataset tools and three enquiry tools matches the apparent purpose.
The dataset tools cover schema discovery, provenance, filtering, searching, comparison, statistics, and ordering—a solid analytical surface. The only notable gap is a way to retrieve all rows at once without a filter, though that may be intentionally omitted since most queries are targeted. The enquiry tools form a complete describe-fields-submit flow.