dataset_topThe highest (or lowest) rows of the Upkeepvo 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?
Without annotations, the description carries the full behavioral burden. It discloses the core ranking behavior and numeric-column requirement, but it does not mention default sort direction, limit handling, tie-breaking, or the output shape, leaving some ambiguity.
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 very short and front-loaded with the key concept. It avoids filler, though it is slightly under-specified rather than being a model of efficient completeness.
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?
There is no output schema and no annotations, so the description alone must fully equip an agent. It fails to clarify default values, limit semantics, or what the returned rows look like, which are meaningful gaps for a ranking tool.
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%, and the description adds meaning mostly by calling out that the column must be numeric and that results can be highest or lowest. It does not explain the limit parameter at all, which is central to how many rows are returned.
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 communicates ranking rows by a numeric column to find the highest or lowest values, which distinguishes it from sibling search/stat/compare tools. It does not explicitly name a sibling but the 'most/least X' phrasing gives a specific query intent.
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' phrase implies the intended use case, and the sibling list helps an agent infer alternatives. However, the description does not explicitly state when not to use this tool or which sibling should be chosen instead.
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 purpose: schema discovery, comparison, provenance, exact row lookup, substring search, stats, and ranking. No two tools overlap in functionality, making misselection unlikely.
All tools follow the consistent pattern 'dataset_' followed by a lowercase noun or verb (columns, compare, provenance, row, search, stats, top). The naming is uniform and predictable.
With 7 tools, the set is well-scoped for a dataset querying server. Each tool covers a distinct query need without redundancy or bloat.
The tools provide comprehensive read-only access to the dataset: schema, row retrieval, search, statistics, ranking, comparisons, and provenance. No obvious gaps for typical analysis workflows.