dataset_topThe highest (or lowest) rows of the Carbikly 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 behavior: ranking rows by a numeric column and returning either the highest or lowest. However, with no annotations available, it does not surface important behavioral details such as default limit behavior, tie handling, or what happens when a non-numeric column is supplied, leaving the description to carry the full transparency burden.
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. The quoted 'which is the most/least X' is somewhat informal and adds little over the first clause, but overall the structure is efficient and easy to parse.
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 annotations, a single sentence about highest/lowest rows is only minimally adequate. An agent still lacks clarity on default row count, asc/desc interplay, return shape, and when this tool is preferable to dataset_stats or dataset_search, so the context is incomplete for reliable 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?
Schema description coverage is only 33%, so the description needs to compensate. It adds the useful constraint that the column must be numeric, but it does not explain the limit parameter or clarify how ascending relates to 'most/least' beyond what the schema already states. The limit behavior is left to inference from the parameter name.
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 returns the highest or lowest rows of a dataset by a numeric column, with the use case 'which is the most/least X'. It is distinct in intent from siblings like dataset_search or dataset_stats, though it does not explicitly name or contrast those alternatives.
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 on when to use this tool versus siblings such as dataset_search, dataset_stats, or dataset_row. The intended use is only implied by the title and phrasing, with no explicit context, prerequisites, or exclusions.
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 addresses a distinct mode of dataset access: schema, provenance, exact match, multi-value comparison, substring search, statistics, and sorting. The only mild overlap is between dataset_row, dataset_compare, and dataset_search, but their descriptions make the filtering differences clear enough.
All tools share a consistent dataset_ prefix and use snake_case, which establishes a clear pattern. The second part is not perfectly uniform—some are nouns like columns and stats, while others are verbs like compare and search—but this is a minor inconsistency.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a meaningful interaction with the dataset without feeling redundant or overwhelming.
The tool surface covers schema discovery, provenance, row retrieval by exact value, multi-value filtering, text search, numeric summaries, and top/bottom ranking. Missing features like arbitrary group-by or pagination are notable but not severe for the apparent read-only exploration purpose.