dataset_topThe highest (or lowest) rows of the Topcoatly 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?
The description discloses the core ranking behavior and the ascending/descending intent through 'highest (or lowest)' and 'most/least X'. However, with no annotations, it does not mention return shape, default limit behavior, tie handling, or whether the operation is read-only.
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 compact and front-loaded, with the key operation stated immediately. The quoted phrase adds a little color but no real bloat; using a clearer verb like 'Returns' would improve it slightly.
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?
With no output schema and no mention of result shape, defaults, or how this tool relates to its siblings, the description leaves an agent to infer important invocation details. It is minimally adequate only for very simple calls.
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 description coverage is only 33%, so the description must compensate for the undocumented column and limit parameters. It adds the 'numeric' qualifier for column and the high/low semantics, but it does not clarify limit defaults or the relationship between ascending and ordering beyond what the schema already states.
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 text clearly conveys a specific operation: selecting the highest or lowest rows of a dataset by a numeric column. It aligns with the title and the intended 'most/least X' behavior, though it does not explicitly differentiate itself from sibling tools.
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?
There is no guidance about when to use dataset_top versus dataset_row, dataset_search, or dataset_stats, and no exclusions or alternative recommendations are given. The only implied context is that the user wants top/bottom rows by a numeric value.
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 query pattern—schema, provenance, exact match, contains search, ordered value comparison, aggregation, and top/bottom ranking. The only mild ambiguity is between dataset_row and dataset_search, but their exact-match versus contains-match descriptions make the boundary clear.
All tools share the consistent dataset_ prefix followed by a clear operation name, and all use lowercase snake_case. The pattern makes the purpose of each tool predictable at a glance.
Seven tools is an appropriate scope for a read-only dataset querying server. Each tool addresses a distinct question type without unnecessary sprawl or redundancy.
The toolkit covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, descriptive statistics, and top/bottom ranking—the core workflows for answering dataset questions. It lacks general range filtering or grouped aggregation, but these are minor gaps for the stated purpose.