dataset_topThe highest (or lowest) rows of the Termslane 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 full burden. It mentions highest/lowest ordering but does not disclose default limit, tie-breaking, handling of missing/non-numeric values, whether it is read-only, or what result shape to expect.
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 sentence with no filler, and the key ranking behavior is front-loaded. It could be phrased more smoothly, but it earns its place without redundancy.
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?
Given no output schema and no annotations, the description leaves essential context missing: what rows look like, default limit, what dataset is targeted, and how it relates to dataset_row or dataset_stats. An agent would need to infer too much.
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 coverage is only 33% (only ascending has a description). The description adds that column must be numeric and implies the ascending parameter, but limit is left entirely undocumented in both schema and description.
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 returns the highest/lowest, which distinguishes it from siblings like dataset_search or dataset_stats. The phrasing is slightly awkward but the core action and resource are identifiable.
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 explicit guidance is given about when to choose this tool over dataset_search, dataset_stats, or dataset_row. The phrase "which is the most/least X" implies a ranking use case, but there is no direct comparison or exclusion.
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.
Most tools target distinct query types: schema, provenance, exact match, substring search, group comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare overlap somewhat (compare is a multi-value variant of row), and dataset_search could be used for the same purpose, but the descriptions clarify the differences well.
All tools follow a clear dataset_ prefix with snake_case names, making the family instantly recognizable. The second part mixes nouns (columns, row, stats, top) and verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped set for a dataset querying server. Each tool addresses a common question type about the Termslane dataset without unnecessary bloat or missing fundamentals.
The toolset covers schema discovery, provenance, exact lookup, substring search, group comparisons, numeric statistics, and top/bottom ranking — a solid set for answering typical dataset questions. A possible gap is lack of pagination or arbitrary row listing, but the search and row tools cover most practical needs.