dataset_topThe highest (or lowest) rows of the Mashzo 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 behavioral disclosure. It mentions the ascending option implicitly but does not disclose the limit cap, tie handling, behavior with non-numeric columns, or output format. Minimal behavioral context beyond the core action.
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, succinct sentence that front-loads the purpose. It contains no unnecessary words or repetition, though it could include more detail without becoming verbose.
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 no output schema and no annotations, the description is incomplete. It does not specify return values, error conditions, the maximum limit (50), or handling of edge cases. An agent would need to infer or test to use it correctly.
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%, and the description adds little beyond that. It hints at the 'ascending' parameter with 'highest (or lowest)' but does not explain the 'column' requirement (numeric) or the 'limit' parameter's cap and default. It fails to compensate for the low 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 states the tool ranks rows by a numeric column and returns the highest or lowest, which clearly identifies its function. It distinguishes from siblings like dataset_search and dataset_stats by focusing on top/bottom rows, though it doesn't explicitly name 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?
No guidance is provided on when to use this tool versus alternatives. The description implies a use case (finding most/least X) but does not mention exclusions or when other dataset tools would be more appropriate.
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.
The tools are largely distinct: schema, provenance, exact lookup, substring search, compare, stats, and top-k. The only mild overlap is among dataset_row, dataset_search, and dataset_compare, since they all retrieve rows, but their descriptions clarify exact match, contains, and value-list comparison respectively.
Every tool uses a consistent `dataset_` prefix with clear snake_case names. Even though some suffixes are nouns and some are verbs, the pattern is uniform and predictable across the entire tool set.
Seven tools is a well-scoped size for a dataset querying server. Each tool covers a distinct common operation without feeling redundant or excessive.
The surface covers the core dataset operations well: schema inspection, provenance, exact lookup, search, comparisons, statistics, and top/bottom ranking. Minor gaps include distinct-value enumeration and grouped aggregation, but these are not fatal for typical dataset questions.