dataset_topThe highest (or lowest) rows of the Kickoffo 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 provided, the description carries the full burden of explaining behavior. It communicates the core behavior: returning the highest or lowest rows ordered by a numeric column. It does not disclose limit defaults, tie handling, null behavior, or the exact shape of the returned result, but the primary ranking behavior is clear.
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 one concise, front-loaded sentence that conveys the ranking semantics without unnecessary padding. The 'most/least' clause is somewhat redundant with 'highest/lowest', but the overall length is appropriate 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?
For a tool with three parameters and no output schema, the description plus schema gives enough to make a reasonable call with `column`, `limit`, and `ascending`. It is less complete on output expectations, defaults, and explicit sibling differentiation, so an agent may need to infer some behavior.
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. It adds useful meaning by specifying that `column` must be numeric and by linking `ascending` to highest/lowest ordering through 'most/least X'. However, it adds no explanation for `limit` beyond the Schema's type/range, leaving part of the parameter semantics unaddressed.
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 names a specific action ('Rank') and resource ('rows ... by a numeric column'), and the 'highest/lowest ... most/least' phrasing makes the ranking intent clear. It is distinguishable from siblings like dataset_search or dataset_stats by focusing on ordering rather than filtering or aggregation, though it does not explicitly name 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?
The phrase 'which is the most/least X' implies a usage context: answering ranking questions over a numeric column. However, the description provides no explicit when-to-use or when-not-to-use guidance, and it does not mention any alternative tool or exclusion condition.
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 operation—schema, provenance, exact lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. The only possible confusion is between dataset_row and dataset_compare, but the descriptions make the multi-value distinction clear.
All tools share a consistent dataset_ prefix and lowercase snake_case style, making the family obvious. The suffixes mix nouns, verbs, and an adjective, so it is not a strict verb_noun pattern but remains predictable.
Seven tools is well-scoped for a dataset querying server; each tool provides a distinct query or metadata capability and none feel redundant.
The toolset covers schema exploration, provenance, exact lookups, text search, comparisons, statistics, and top/bottom ranking. A direct group-by or unique-values tool would improve grouped aggregation workflows, but the main querying surface is well covered.