dataset_topThe highest (or lowest) rows of the Turndownly 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 full behavioral burden. It discloses only that ranking is involved; it does not state whether the operation is read-only, what the output shape is, how limit defaults work, or how ties/edge cases are handled.
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?
Single sentence with a useful user-question framing; every phrase earns its place and the core behavior is front-loaded.
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?
Adequate for a simple ranking tool with an informative schema, but the absence of annotations and output schema means the description alone leaves return format and limit behavior under-specified.
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?
The description adds the useful constraint that `column` must be numeric, which the schema does not say. However, with schema coverage at only 33%, it does not compensate for the undocumented `limit` parameter and only indirectly maps `ascending` to lowest-first.
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 names a specific operation—returning the highest or lowest rows ranked by a numeric column—and frames it as answering 'which is the most/least X'. This clearly differentiates it from siblings like dataset_stats or dataset_search.
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 implies when to use the tool, but there is no explicit guidance on when to prefer it over dataset_stats, dataset_search, or dataset_row, and no exclusion conditions.
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 clear primary purpose: schema, provenance, exact lookup, substring search, comparison, stats, and top/bottom ranking. There is minor overlap between dataset_row and dataset_compare for single-value lookups, but the descriptions steer usage toward distinct cases.
All tools share the dataset_ prefix and use a consistent noun or verb suffix after it. The pattern is predictable and makes it easy to infer what each tool does.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool addresses a different query type without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, substring search, comparisons, numeric statistics, and ranking. Minor gaps like listing all unique values or arbitrary numeric filtering are absent, but they can be worked around with the existing tools.