dataset_topThe highest (or lowest) rows of the Dsarvo 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions highest/lowest rows and numeric columns, but does not disclose default ordering, behavior when values are tied or missing, whether the result is sorted output or ranked with rank values, or what the return shape looks like. These are important gaps for a tool with no annotation safety context.
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 a useful exemplar phrase. It is not bloated, though the odd 'Dsarvo' wording and the em-dash aside add mild clutter without much extra information.
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?
Given the lack of annotations and output schema, the description is incomplete. An agent cannot tell the default limit, how ascending interacts with 'highest/lowest' beyond the schema's brief note, or what the output rows contain. The description works for basic selection but leaves important operational details unspecified.
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?
With only 33% schema description coverage, the description partially compensates by clarifying that 'column' must be numeric and that highest/lowest corresponds to ranking direction. However, the 'limit' parameter is not described at all, and the description does not explain how limit interacts with the ranking or what the default is.
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 it returns the highest or lowest rows of the Dsarvo dataset by a numeric column, and the title reinforces the ranking action. It is distinguishable from sibling tools like dataset_search, dataset_stats, and dataset_row, though it does not name them explicitly. The typo 'Dsarvo' and informal phrasing slightly weaken precision.
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 use case is implied through the phrase 'which is the most/least X', which guides an agent toward ranking questions. However, there is no explicit guidance on when to use this tool instead of siblings such as dataset_search or dataset_stats, and no when-not-to-use conditions are provided.
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.
The metadata and numeric tools (columns, provenance, stats, top) are clearly distinct, but dataset_row, dataset_search, and dataset_compare all retrieve rows by value, so an agent could initially confuse exact-match, contains-search, and multi-value ordering. The descriptions do clarify the boundaries, making the overlap manageable.
All tools share the consistent dataset_ prefix and lowercase style, but the second part mixes nouns (row, columns, provenance, stats) with verbs (compare, search, top). This is predictable enough, though not a strict verb_noun pattern.
Seven tools is well-scoped for a single-dataset server: schema discovery, provenance, three retrieval modes, statistics, and ordering each earn their place without bloat or thinness.
The tool surface covers the main workflows: learn the schema, attribute correctly, look up exact/contains/multi-value rows, compute numeric summaries, and find top/low values. No obvious dead-end operations are missing for the apparent purpose.