dataset_topThe highest (or lowest) rows of the Xlifflane 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 full responsibility for behavioral disclosure. It mentions highest/lowest ordering but does not explain what a returned row looks like, how ties are handled, what the default limit is, or whether full rows or just the ranking column are returned. This is a significant gap for a ranking tool.
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, short sentence that front-loads the core action. The appended quote 'which is the most/least X' is somewhat redundant, but overall the description has no wasted words.
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 three parameters, no output schema, and no annotations, the description is too sparse. It omits return value format, default limit behavior, tie-breaking, and error conditions, so an agent may call it correctly but cannot anticipate results reliably.
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%, with only 'ascending' having a description. The description adds that 'column' must be numeric and clarifies highest/lowest ordering, but it does not explain the 'limit' parameter's meaning or default behavior. It only partially compensates for the low schema coverage.
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 the tool ranks rows by a numeric column and identifies highest/lowest rows, which is a specific verb-resource pairing. It is distinct from sibling tools like dataset_search or dataset_stats, though it does not explicitly name them.
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 usage context is implied: use this when you want the most/least values of a numeric column. However, there is no explicit guidance about when not to use it or which sibling tool would be a better alternative for other ranking or filtering needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a distinct purpose: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/lowest rows. The main potential confusion is between dataset_row and dataset_compare, since both handle exact value matching, but the multi-value ordering intent of dataset_compare keeps them separable.
All tools share a consistent dataset_ prefix followed by a clear operation or noun: columns, compare, provenance, row, search, stats, top. The naming pattern is uniform and predictable.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool covers a distinct need without redundancy, and the count feels neither sparse nor bloated.
The set covers schema inspection, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom ranking. A minor gap is the lack of a tool to retrieve all rows or page through large result sets, but the existing tools are sufficient for most dataset questions.