dataset_statscount, min, max, mean, median and sum of a numeric column of the PerDiemDesk dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
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 burden of behavioral disclosure. It usefully reveals that grouping commas and currency are normalized and that non-numeric rows are excluded and counted, which are non-obvious behaviors an agent needs to know.
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?
A single sentence conveys the full metric set and the important data-handling caveats. Every element is useful, and the key output names are front-loaded before the behavioral notes.
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 one-parameter, low-complexity tool, the description covers selection and invocation well. The main gap is the absence of an output schema and no mention of the return format, leaving the exact structure of the results slightly unspecified.
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 defines 'column' as a string with minLength 1, and schema description coverage is 0%, so the description must add meaning. It clarifies that the parameter should be a numeric column of the PerDiemDesk dataset, but it does not provide examples, exact column-name expectations, or how to discover valid column names.
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 the concrete operation—computing count, min, max, mean, median, and sum—for a numeric column of a specific dataset. It clearly differentiates from siblings like dataset_columns and dataset_search by specifying the statistical aggregation scope.
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 use for summarizing numeric columns in the PerDiemDesk dataset and notes non-numeric rows are excluded. However, it does not explicitly state when to prefer this tool over sibling tools or mention any circumstances where it shouldn't be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.