dataset_topThe highest (or lowest) rows of the Netsheetly 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 behavioral disclosure burden. It does communicate the read-only ranking behavior and the highest/lowest orientation, but it omits details such as default ordering, tie handling, behavior for non-numeric columns, and the output shape. The schema covers defaults and bounds, but the description itself adds limited behavioral context.
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 concise sentence with the key concept front-loaded. It avoids redundant restatement of the schema, though the em-dash quote construction is slightly informal and could be tightened without losing meaning.
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 the low parameter coverage and no output schema, the description is only minimally complete. The schema fills in parameter constraints, but the description does not clarify return shape, default limit behavior, or when the tool is the right choice versus siblings. It is adequate for a simple tool but leaves meaningful gaps.
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%, so the description must compensate. It adds meaning for the column parameter by specifying it must be numeric, and 'highest (or lowest)' maps to the ascending flag. However, it does not explain the limit parameter or its maximum of 50, which is a notable gap for a top-N tool.
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 operation: returning the highest or lowest rows of the dataset by a numeric column, framed as answering 'which is the most/least X'. This distinguishes it from siblings like dataset_search, dataset_row, or dataset_stats, which address filtering, single-row lookup, or aggregation rather than ranking.
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 description implies usage for top-N or bottom-N questions via 'most/least X', but it does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or conditions. Usage context is present but only by implication.
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 targets a distinct operation: schema, provenance, exact row lookup, substring search, ordered comparison, statistics, and top/bottom extremes. dataset_row and dataset_compare are somewhat similar, but their descriptions make the single-value vs ordered-multi-value distinction clear.
All tools share the consistent dataset_ prefix and snake_case style, making the family recognizable. However, the second segment mixes noun, verb, and adjective forms (columns, compare, top), so the naming is not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a different common question type without redundancy, and the count feels neither thin nor bloated.
The tool set covers schema discovery, provenance, exact lookup, substring search, comparisons, summary statistics, and top/bottom ordering. For a read-only dataset QA server, there are no obvious missing operations or dead ends.