dataset_compareThe rows of the DailyLogDesk 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, the description carries the behavioral disclosure burden. It reveals two non-obvious behaviors: matching is disjunctive ('any of the given values') and output order follows the supplied value order, not the dataset's natural order. It does not detail exact-match or case-sensitivity behavior, but the core matching semantics are transparent.
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 purposeful sentence that front-loads the selection behavior and ends with the intended use case. There is no filler, repetition, or unnecessary detail.
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 with no output schema, the description covers both inputs, the matching rule, ordering, and the intended question type. It omits explicit guidance about alternatives and edge cases, but these are minor given the tool's simplicity and the available sibling names.
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 supply meaning. It connects 'column' to a DailyLogDesk field and 'values' to the list of values to match, including the ordering implication. It does not explain constraints like minItems/maxItems or value format, although the schema covers those structurally, so the compensation is partial rather than complete.
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 DailyLogDesk rows matching any of the given column values, in the order provided, and ties this to 'X vs Y' comparison questions. It is specific about the resource and selection semantics, though it lacks an explicit verb like 'return' or 'compare' and does not name sibling tools for differentiation.
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' gives a clear intended context: comparing two or more specific values in the same column. It does not explicitly contrast with dataset_search or dataset_row, and it omits exclusions, but the use case is concrete enough for an agent to select it appropriately.
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 has a clearly stated query mode, but several return rows from the same dataset with overlapping semantics (exact match, substring search, multi-value compare, top-N). An agent could sometimes confuse dataset_row and dataset_search, though the descriptions provide enough detail to disambiguate.
All tools follow a consistent dataset_ prefix pattern with short, descriptive operation names. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct access pattern without unnecessary duplication or bloat.
The toolset covers schema discovery, provenance attribution, exact row lookup, substring search, value comparison, numeric statistics, and top/bottom ranking. For a read-only dataset querying server, this is a complete surface with no obvious dead ends.