dataset_compareThe rows of the WalkthroughDesk 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 full behavioral burden. It usefully discloses retrieval semantics: rows are matched when the column equals any of the given values, and the ordering follows the values as provided. However, it does not describe the output shape, read-only nature, limits, or edge cases such as missing values.
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?
A single dense sentence that front-loads the dataset and filter behavior, then adds the ordering guarantee and the intended use case. There is no filler or repetition of schema constraints.
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 and no annotations, the description covers what is returned, the filtering key, accepted values, ordering, and when to use it. It could include an example or explicitly route around sibling tools, but the essential information for correct invocation is present.
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 descriptions are 0%, so the description provides essential parameter meaning: 'column' is the field to filter on and 'values' are the accepted alternatives, with their order driving the output order. This is significant added meaning beyond the bare schema, though details like value format and exact-match semantics are left implicit.
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 of the WalkthroughDesk dataset filtered by a column matching any of the given values, in the provided order. It implies a comparison use case via 'X vs Y', but it does not explicitly differentiate itself from sibling tools by name.
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 use case: comparing a small set of specified values side by side. It does not explicitly mention alternatives or exclusions, but the usage context is strong and distinctive.
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 clear role: schema, provenance, exact row lookup, substring search, compare, stats, and top/bottom rows. dataset_row and dataset_compare both filter rows, but the distinction between exact single-value lookup and ordered multi-value comparison is clear enough from the descriptions.
All tools follow a consistent dataset_<noun> pattern, making the tool family immediately recognizable and predictable. No mixed styles or vague verbs are present.
Seven tools is well-scoped for a dataset exploration server: schema, provenance, lookup, search, comparison, stats, and ranking cover the core operations without unnecessary redundancy.
The tool surface covers schema discovery, provenance, exact lookup, substring search, comparison, numeric statistics, and ordering, which covers most common dataset questions. Minor gaps exist such as no general multi-condition filtering or pagination for search results, but agents can work around these.