dataset_topThe highest (or lowest) rows of the Orgplanly 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 at all, the description carries the full burden, but it only restates highest/lowest ordering. It does not disclose behavior on ties, missing/null values, non-numeric columns, or the shape of the returned rows, which an agent would need to anticipate.
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 front-loaded sentence with no fluff, and the title reinforces the operation. Minor awkwardness in the em-dash phrasing keeps it from being a 5.
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 with no output schema and no annotations, the description should say what the returned result looks like and clarify edge behavior. It currently leaves output shape and fallback behavior undefined, and the sibling context is not addressed.
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%, and the description helps by clarifying that column must be numeric and that ascending corresponds to lowest-first. However, it adds nothing about limit or any additional constraints beyond what the schema already specifies.
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 states a specific operation: returning the highest or lowest rows of the Orgplanly dataset ranked by a numeric column. It is clear, but it does not explicitly contrast with siblings like dataset_stats or dataset_search, so differentiation is left to inference.
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 guidance is given on when to choose this tool over the sibling tools, nor when not to use it. The intended use is implied by the title and 'most/least' phrasing, but no alternatives or exclusions are named.
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 maps to a distinct query mode—schema, provenance, exact match, substring search, ordered multi-value comparison, numeric stats, and top/bottom ranking—so an agent can generally choose based on question type. The only mild overlap is between dataset_row and dataset_compare for single-value lookups, but the wording clarifies exact equality versus ordered multi-value matching.
All seven tools share the dataset_ prefix and consistent snake_case, making the family instantly recognizable. The suffix varies between noun-like and verb-like forms, but the pattern remains predictable and readable across the whole set.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a necessary operation without redundancy, and none feel superfluous or missing.
The toolset covers the full range of common data exploration needs for the Orgplanly dataset: schema discovery, provenance attribution, exact lookups, substring search, comparisons, numeric summaries, and ranked extremes. For a read-only dataset server, there are no obvious dead ends or significant gaps.