dataset_topThe highest (or lowest) rows of the Attestvio 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 burden of behavioral disclosure. It explains that rows are ordered by a numeric column and can return either highest or lowest, which covers core behavior. It omits whether the result is a single row or list, default limit behavior, and whether the operation is read-only.
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 short, front-loaded with the core action, and uses the parenthetical 'most/least X' to clarify the use case. Every word earns its place, though it could add a bit more behavioral detail without becoming verbose.
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 output schema and no annotations, the description conveys the essential ranking concept but leaves ambiguity around return format, default limit, and boundary conditions. Given the simple domain, it is minimally adequate but not fully complete for an agent invoking it without additional context.
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 description coverage is only 33% (only 'ascending' has a description). The description adds that 'column' should be numeric, which is useful, but it says nothing about the 'limit' parameter, its default, or how it interacts with ranking. The description only partially compensates for the missing schema documentation.
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 title 'Rank rows by a numeric column' and description 'highest (or lowest) rows ... by a numeric column' state a clear verb and resource. It does not explicitly differentiate from siblings like dataset_search or dataset_stats, but the ranking behavior is distinct enough for an agent to grasp the core action.
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 'which is the most/least X' implies the intended use case for ranking questions, providing some context. However, there is no explicit when-to-use guidance or mention of alternative sibling tools such as dataset_search or dataset_stats.
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, aggregation, and top/bottom ranking. The only possible confusion is between dataset_row and dataset_compare, since both do exact value matching, but compare's ordering/multiple-value purpose is sufficiently clarified.
All tools share the consistent dataset_ prefix and use lowercase snake_case, which makes them easy to group. The second token mixes nouns (columns, row, stats, top, provenance) with verbs (compare, search), so it is not a uniform verb_noun pattern but remains predictable.
Seven tools is well-scoped for a dataset querying server: every tool covers a distinct aspect of data exploration without redundancy. The count is neither too thin nor too heavy for the stated purpose.
The surface covers the main lifecycle of dataset exploration: schema, provenance, exact/match lookups, search, comparison, stats, and ranking. Minor gaps such as pagination beyond 50 results or arbitrary multi-column filters would improve completeness but are not fatal.