dataset_topThe highest (or lowest) rows of the Sbarvo 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?
There are no annotations, so the description carries the full behavioral burden. It discloses only that ranking is by a numeric column and can be highest or lowest; it does not state default ordering, the meaning of limit, behavior on non-numeric columns or ties, or the shape of the returned rows.
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 wasted words. It is terse without being rambling, though it could have included a parenthetical on the default ordering without losing conciseness.
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?
Given the absence of annotations and an output schema, the description is too thin. An agent still cannot predict the return format, default limit behavior, or error handling, and the lack of sibling differentiation means it may invoke dataset_stats instead for similar questions.
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 undocumented parameters. It adds meaning for 'column' (must be numeric) and partially for 'ascending' (highest vs lowest), but it says nothing about 'limit', leaving a key parameter semantically empty.
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 a ranking operation: get the highest or lowest rows of the dataset by a numeric column. The gloss 'which is the most/least X' makes the purpose concrete and distinguishes it from column-listing, stats, or search siblings, though it does not explicitly call out the difference.
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 a natural use case ('which is the most/least X') but gives no explicit when-to-use or when-not-to-use guidance. It doesn't mention alternatives like dataset_stats or dataset_search, so the agent must infer the appropriate context from the tool name and 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, but dataset_row and dataset_compare can overlap when a single value is used, since both retrieve rows by column equality. dataset_search is distinct because it searches across all cells rather than a specific column.
All tools share the consistent dataset_ prefix, but the second part mixes nouns (columns, provenance, row, stats) and verbs (compare, search, top). The pattern is still predictable and readable, so it is only a minor deviation.
Seven tools is well-scoped for querying a single dataset: schema, provenance, exact lookup, substring search, ordered comparison, statistics, and ranking each earn their place. No tool feels redundant or excessive.
The tool surface fully covers read-only exploration of the Sbarvo dataset: learning the schema, checking provenance, retrieving rows by exact match or substring, comparing values, computing statistics, and finding top/bottom rows. No obvious missing operation for the stated domain.