dataset_statscount, min, max, mean, median and sum of a numeric column of the Monthendly 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, the description carries the full burden and discloses meaningful behaviors: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It does not clarify how excluded rows are represented in the result or how the statistics are returned, but the disclosed data-cleaning behavior adds real value.
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?
One compact sentence that front-loads the computed statistics and appends behavioral caveats in a parenthetical. Every word earns its place with no redundancy.
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 tool is simple (one parameter, no nested objects), but with no output schema and no annotations, the description should say something about the return shape or behavior on empty/non-numeric input. The stats list is complete, yet the result representation is left 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?
Schema description coverage is 0%, so the description must compensate. It adds that the column must be numeric and belong to the Monthendly dataset, which goes beyond the bare schema ('column': string). It does not specify format expectations such as exact name matching, but for a single parameter this is adequate.
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 a specific operation (compute count, min, max, mean, median, sum) on a specific resource (a numeric column of the Monthendly dataset). The enumerated statistics clearly distinguish it from siblings like dataset_row, dataset_search, and dataset_columns.
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 intended use is implied by the title and the statistic list — an agent can infer this is the tool for descriptive statistics. However, there is no explicit guidance about when not to use it or how it compares to alternatives such as dataset_top or dataset_search.
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 has a distinct purpose (schema, provenance, exact match, substring search, multi-value compare, stats, top/bottom), and descriptions clearly differentiate them. The only mild overlap is between dataset_row and dataset_compare, but the exact-match vs multi-value distinction is explicit enough to avoid serious confusion.
All tools share a consistent 'dataset_' prefix in snake_case, which creates a clear family identity. However, suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is not perfectly uniform verb_noun.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query pattern without redundancy or bloat, and the count sits comfortably within the ideal 3–15 range.
The tool surface covers the core needs for working with a dataset: schema discovery, provenance, exact lookup, full-text search, comparisons, numeric aggregates, and top/bottom ordering. Minor gaps exist, such as no dedicated count-by-filter or multi-column filtering, but agents can work around these using existing tools.