dataset_topThe highest (or lowest) rows of the Soapvo 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 explains the ranking intent but does not mention that the tool just reads data, that results are limited, that ascending defaults to false, or what fields are returned. Several important behaviors are left to the schema or to inference.
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 front-loaded sentence with no filler, and the quote makes the intent immediately relatable. It is short, though the brevity contributes to the semantic gaps in other dimensions.
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 essential ranking semantics are present, but with no output schema and no annotations, an agent is left guessing about return shape, default limit, and edge-case behavior. For a simple top-N tool this is borderline adequate but not complete.
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 only 33% (just ascending), so the description should compensate for column and limit. It adds that column must be numeric and that ascending/descending corresponds to lowest/highest, but it never explains the limit parameter or the default/behavior of ascending beyond the schema's one-line note.
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 uses 'highest (or lowest) rows... by a numeric column' with a concrete user question ('which is the most/least X'), clearly conveying a ranking operation. It distinguishes dataset_top from statistical/summary tools such as dataset_stats and from arbitrary row lookup in dataset_row, though it doesn't explicitly name a 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 'which is the most/least X' phrasing gives a clear context for ranking questions, but the description provides no explicit when-to-use/where-not-to guidance or alternatives among the listed sibling tools. It implies usage rather than stating it.
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 targets a distinct operation type (schema, provenance, exact lookup, substring search, multi-value compare, stats, top/bottom), so boundaries are mostly clear. dataset_compare is slightly vague by name but its description distinguishes it from dataset_row and dataset_search.
All tools follow a predictable `dataset_<topic>` snake_case pattern. Even though some suffixes are nouns and some are verbs, the uniform prefix and lowercase underscore style make the set feel consistent.
Seven tools is a well-scoped size for a single-dataset exploration server. Each tool serves a clear querying or metadata need without redundancy.
The toolkit covers schema, provenance, exact match, search, multi-value comparison, numeric stats, and top/bottom rows—a broad and practical surface. Obvious missing pieces are distinct-value enumeration and group-by aggregates, but most common questions can be answered with the existing tools.