dataset_topThe highest (or lowest) rows of the Rebadgo dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
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 burden of behavioral disclosure. It does state the core read-oriented behavior: returning rows ranked by a numeric column. However, it does not disclose result shape, tie-handling, default ordering behavior beyond the basic highest/lowest idea, or any additional 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 concise sentence with no filler. It is slightly redundant with the title, but it is front-loaded 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?
For a tool with no annotations and no output schema, the description is too thin. It leaves out the meaning of `limit`, the need for a numeric column is only implied, and there is no mention of what the returned data looks like or how it differs from sibling tools.
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 coverage is low at 33%, and the description compensates only partially. It clarifies that `column` should be numeric and that ascending relates to highest/lowest, but it never explains the `limit` parameter or how many rows will be returned. The name `dataset_top` implies a limit, but the description does not state it.
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 identifies a clear, specific operation: retrieving the highest or lowest rows of the Rebadgo dataset by a numeric column. It conveys the ranking intent precisely and differentiates the tool from siblings like dataset_row, dataset_stats, and dataset_search.
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 no guidance about when to use this tool versus alternatives. It does not mention which sibling tools would be better for different needs, such as when to use dataset_stats instead of dataset_top.
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 serves a clearly distinct purpose: schema exploration, provenance, exact row lookup, substring search, aggregate stats, top/bottom rows, and value comparisons. Even the similar-looking row and search tools differ in exact match vs. substring match, so agents can reliably choose the right one.
All tools share the consistent prefix 'dataset_' followed by a single, descriptive word (columns, compare, provenance, row, search, stats, top). This uniform pattern makes the tool set predictable and easy to navigate, satisfying the consistency requirement even though the suffix is not strictly verb_noun.
Seven tools is well within the ideal range and each one covers a distinct query type for the dataset domain. The count feels neither sparse nor bloated, and every tool has a clear use case.
The set covers metadata, provenance, exact/pattern matching, statistics, ordering, and comparative lookups, which handles most common dataset questions. A minor gap is the absence of a tool to retrieve the full dataset or list distinct values, but agents can work around these with existing tools.