dataset_topThe highest (or lowest) rows of the Attestroom 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 only says rows are ranked by a numeric column, with no mention of whether the operation is read-only, how ties are handled, what happens with missing or non-numeric values, or whether a default limit applies.
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, and the 'most/least X' framing is memorable and useful. The only issue is minor punctuation and quoting awkwardness, but the structure is otherwise appropriately concise.
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?
The description, combined with the schema, is adequate for making a basic top-N call: it names the dataset, the numeric column requirement, and the ordering direction. However, there is no output schema, and the description does not clarify what the returned rows look like or what the default limit behavior is, leaving some ambiguity for a full understanding.
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?
The schema only describes the `ascending` parameter, so the description partially compensates by clarifying that `column` must be numeric and that 'highest/lowest' maps to ordering direction. However, the `limit` parameter is left semantically unexplained, and the description does not fully fill the gaps left by the low 33% schema coverage.
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 that this tool returns the highest or lowest rows of the Attestroom dataset ordered by a numeric column. It distinguishes itself from sibling tools like dataset_row (single row), dataset_stats (aggregates), and dataset_search (filtering) by focusing on ranking/top-N behavior.
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 this tool is for extreme-value ranking questions, giving some usage context. However, it does not explicitly state when to use this tool over dataset_stats or dataset_search, nor does it mention any exclusions or alternative tools.
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 lookup, substring search, stats, and top/bottom queries. dataset_row and dataset_compare overlap somewhat since both filter on column values, but the descriptions clarify exact single-value matching versus ordered multi-value comparison.
All tools consistently share the dataset_ prefix and use lowercase snake_case, which makes the set feel unified. However, suffixes are a mix of nouns (columns, provenance, row, stats) and verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a distinct common operation without feeling padded or redundant.
The set covers the essential dataset operations: schema discovery, provenance, exact match, text search, numeric statistics, ranking, and multi-value comparison. Minor gaps exist such as pagination for search results and range-based numeric filters, but agents can generally answer common questions without dead ends.