dataset_topThe highest (or lowest) rows of the PerDiemDesk 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 only states that rows are ranked by a numeric column, but fails to mention how ties are handled, what happens with non-numeric columns, whether nulls are excluded, or what the default limit is. The schema hints at limit and ascending, but the description adds no additional behavioral context.
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 concise sentence with an illustrative example. It is front-loaded with the core action (ranking rows). While it is not structured, it is appropriately brief and avoids unnecessary verbosity.
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 is too sparse. It does not mention the output format (e.g., which columns are returned, order), default limit, error conditions, or any constraints. An agent would have to infer many details, making it incomplete for confident 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% (only 'ascending' has a description). The tool description does not explain 'column' or 'limit' beyond what the schema already states. It adds a hint that the column should be numeric, but that's already implied by the title. For a tool with low schema coverage, the description should compensate but doesn't.
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 ranks rows of a specific dataset (PerDiemDesk) by a numeric column, returning the highest or lowest rows. The phrase 'which is the most/least X' provides a concrete usage example. This distinctly separates it from siblings like dataset_search (searching) and dataset_stats (aggregates).
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 given on when to use this tool versus alternatives. The description does not mention any conditions for selecting this over dataset_stats or dataset_search, nor does it exclude any scenarios. The agent is left to infer when ranking top/bottom rows is 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.
Each tool targets a distinct query pattern—schema, provenance, exact rows, substring search, stats, top values, and multi-value comparisons—so an agent can generally choose correctly. Some overlap exists between dataset_row and dataset_compare (both filter rows by column values), and dataset_search overlaps with dataset_row on substring matches, but the descriptions are clear enough to resolve the ambiguity.
All tool names follow the same dataset_<operation> pattern, with clear nouns like columns, row, search, stats, top, compare, and provenance. The naming is uniform and predictable, with no mixed conventions.
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary bloat.
The tool surface fully covers the read-only dataset workflow: schema inspection, provenance, exact lookup, substring search, statistical summaries, extreme-value ranking, and side-by-side comparisons. No obvious gaps would prevent an agent from answering typical questions about this dataset.