dataset_topThe highest (or lowest) rows of the DoorsetBook 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 burden of explaining behavior, and it does state the core behavior: returning top or bottom rows based on a numeric column. However, it does not disclose edge cases such as non-numeric input, tie handling, return shape, or the default limit behavior.
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 a compact use-case paraphrase. There is no filler or repetition, and the key information appears immediately.
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 definition is usable for a simple top-k query: it identifies the column type and that rows are returned, and the schema covers limit bounds and ascending direction. But with no output schema and no annotations, details like the default limit, returned fields, and edge-case behavior remain undocumented.
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 compensates for 'column' by specifying it must be numeric and for direction via 'highest/lowest', but the 'limit' parameter's meaning is left to its name and min/max bounds rather than explained.
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 identifies the action and resource: returning the highest or lowest rows of the DoorsetBook dataset by a numeric column. The 'most/least X' phrasing maps directly to the ranking use case and distinguishes it from siblings like dataset_search or dataset_stats.
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 provides a concrete decision trigger for when to use this tool. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5.
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 has a distinct primary purpose—schema, provenance, exact lookup, multi-value comparison, search, stats, and top rows. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by column value, though dataset_compare is specifically for ordered multi-value comparisons.
All tools share the consistent dataset_ prefix and snake_case naming, making them easy to group. However, the suffix style is mixed: some are nouns (columns, row, stats), some are verbs (compare, search), and one is an adjective (top), which is a minor deviation from a fully uniform verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool covers a meaningful query need—schema discovery, provenance, exact lookup, comparison, search, statistics, and ranking—without unnecessary bloat or missing core access patterns.
The tool surface covers the main ways to interact with the DoorsetBook dataset: schema, metadata, exact and fuzzy lookup, comparisons, aggregates, and top/bottom rows. Minor gaps exist, such as no explicit pagination for large result sets or numeric range filtering, but common questions are well supported.