dataset_topThe highest (or lowest) rows of the Reviewvo 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 present, so the description must carry the burden. It does not mention whether the operation is read-only, whether it modifies data, or any side effects. Since it is likely a query tool, this omission reduces transparency.
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 extremely concise, using one clear sentence with an illustrative phrase. There is no redundancy or extraneous content; it efficiently conveys the core functionality.
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 description is adequate for a simple top-N query tool, but lacks usage context, parameter details, and any mention of output format. Given the siblings and the low schema coverage, more context would be needed for an agent to reliably select and invoke this tool correctly.
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?
The schema has a description only for the 'ascending' parameter; 'limit' and 'column' have no descriptions. The tool description does not add any meaning to these parameters, leaving the agent to guess their roles (though names are somewhat self-explanatory). Coverage is low and description does not compensate.
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 indicates the tool returns the highest or lowest rows based on a numeric column, with a helpful example phrase. It does not explicitly state 'returns' but the intent is unambiguous. It differentiates from search/stats by focusing on ranking, though not explicitly naming siblings.
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?
No guidance is provided on when to use this tool versus the alternatives like dataset_search or dataset_stats. The description gives a sense of 'which is most/least' but does not contrast with sibling tools or state conditions for selection, leaving the agent to infer the appropriate context.
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: schema, provenance, exact lookup, substring search, multi-value comparison, aggregate stats, and ranking. Minor overlap exists between dataset_row and dataset_compare (both match column values) and between dataset_columns and dataset_provenance (both expose provenance details), but the descriptions make the intended use clear.
All tools share the dataset_ prefix and use lowercase snake_case, giving a clear family identity. The suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), which is a minor inconsistency, but the overall pattern is predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool has a distinct role and none feels redundant or unnecessary for the core task of exploring and analyzing the Reviewvo dataset.
The surface covers schema discovery, provenance, exact/string lookup, multi-value comparison, numeric summaries, and ranking, which covers the main ways an agent would explore a dataset. Minor gaps include no pagination for large result sets and no multi-column filter, but these are workable given the available tools.