dataset_topThe highest (or lowest) rows of the HomeCover HQ 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 burden of behavioral disclosure. It states that rows are ranked by a numeric column, but it does not mention limit behavior, default ordering, output shape, or whether ties or invalid columns have special handling. These are material gaps for an agent calling this tool.
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 short, front-loaded, and free of filler. It communicates the core purpose in one sentence and even adds a user-facing framing of the problem. It could be slightly more structured, but it is appropriately concise for a simple tool.
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?
There is no output schema and no annotations, so the description must cover more than it does. It omits what the result looks like, how limit behaves, and any ordering/tie details, making the tool underspecified for reliable autonomous invocation.
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?
With only 33% schema description coverage, the description partially compensates by clarifying that column must be numeric and that highest/lowest maps to ordering. However, it does not explain the limit parameter or its default/effect beyond the schema's min/max constraints.
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 title and description clearly state the operation: rank/top rows of the HomeCover HQ dataset by a numeric column, supporting highest or lowest. This distinguishes it from sibling tools like dataset_row, dataset_search, and dataset_stats, which serve different lookup or aggregation purposes.
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 the main use case through the phrase 'which is the most/least X', but it does not explicitly state when to choose this tool over alternatives like dataset_search or dataset_stats. There is no when-not-to-use guidance or comparison with siblings.
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 have clearly distinct purposes: schema, provenance, exact row lookup, substring search, comparison, stats, top values, and enquiry steps are all separate. The only mild ambiguity is between dataset_row, dataset_search, and dataset_compare, but their descriptions clarify exact matching, substring matching, and ordered value comparison respectively.
The dataset_* prefix and enquiry_* prefix create a clear grouping. Within each group the pattern is mostly consistent, though some names are noun-based (dataset_columns, dataset_provenance) while others are verb-based (dataset_search, dataset_compare), and submit_enquiry reverses the prefix order.
Ten tools is a well-scoped set for this domain: seven query tools cover the dataset surface and three cover the enquiry flow. Each tool has a distinct job and none feel redundant.
The dataset side covers schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking, which covers the full range of likely questions. The enquiry side handles explaining the process, listing fields, and submitting with a two-step confirmation, leaving no obvious dead ends.