dataset_statscount, min, max, mean, median and sum of a numeric column of the Dispatchzo 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 disclosure and does a good job: it reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It doesn't mention edge cases like empty columns or the exact output shape, but the listed statistics plus these behavioral notes are substantial.
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 tight sentence that packs the output list and key behavioral details into an efficient parenthetical. There is no redundancy, and the title complements rather than repeats the description.
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?
Given the tool's simplicity—one parameter and no output schema—the description is mostly complete: it enumerates the six statistics and explains how malformed data is treated. It would be slightly better if it specified the output format or behavior when there are no valid numeric rows, but those are minor gaps.
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 input schema only provides a `column` string with no description (0% schema description coverage), so the description must compensate. It does so by clarifying that the column must be numeric and that non-numeric rows are handled. For a single simple parameter, this is sufficient to guide invocation, though it doesn't specify valid column names or case sensitivity.
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 title and description clearly identify the tool as computing summary statistics for a numeric column of the Dispatchzo dataset, listing the exact statistics returned (count, min, max, mean, median, sum). It is distinct from sibling tools like dataset_columns or dataset_top, though it does not explicitly name them.
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 usage by mentioning 'a numeric column', and the parenthetical about commas/currency and non-numeric rows gives context about when the tool is useful. However, it never explicitly states when to prefer it over alternatives such as dataset_top or dataset_search, nor does it provide exclusions.
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 need: schema, provenance, exact row lookup, substring search, multi-value ordered comparison, numeric stats, and top/bottom ranking. There is no meaningful overlap that would make an agent uncertain which tool to call.
All tools follow a consistent `dataset_` prefix with concise snake_case names that clearly reflect their function. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped set for a dataset-querying server. Each tool covers a distinct operation and none are redundant or superfluous.
The toolset covers schema discovery, provenance, exact and substring search, comparison, statistics, and ranking—strong coverage for a dataset Q&A server. Minor gaps exist such as no direct way to fetch all rows, list distinct values, or filter on multiple columns simultaneously, but these can usually be worked around with the existing tools.