dataset_topThe highest (or lowest) rows of the Ninebix 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, and the description does not clarify read-only status, side effects, or the exact structure of the returned rows. It only hints at sorting behavior via the ascending parameter, leaving operational details unclear.
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, directly to the point, with no redundant words or filler. It efficiently conveys the core function.
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 tool is simple, but lacks an output schema and does not mention whether it returns entire rows or just column values. It gives enough for basic usage but leaves some ambiguity about the result structure.
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 33% (only ascending has a description). The description adds context that 'column' must be numeric, which helps, but does not explain limit semantics or the output format, and the schema remains minimal.
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, either highest or lowest, and even provides a natural language query example. This 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 description implies usage when needing top or bottom rows by a metric, but does not explicitly mention when to avoid this tool in favor of siblings. There is no direct comparison to dataset_stats or dataset_row.
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, row retrieval, search, stats, top values, comparisons, and provenance. No overlap or ambiguous responsibilities.
All tools follow the consistent `dataset_<action>` pattern with clear verb-like suffixes, making the set predictable and easy to navigate.
Seven tools cover the full range of data exploration needs without being excessive. The count is well within the ideal range for a focused dataset server.
The toolset provides comprehensive coverage: schema inspection, individual rows, search, statistics, top/bottom sorting, comparisons, and metadata. No obvious missing capability for typical dataset queries.