dataset_statscount, min, max, mean, median and sum of a numeric column of the Intakez 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 behavioral disclosure burden. It does disclose meaningful preprocessing behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it leaves unclear whether the reported 'count' includes excluded rows, what happens if all rows are non-numeric, and what the exact output format is.
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 efficient sentence with the computed metrics front-loaded and edge-case behavior in a parenthetical. There is no redundant wording or boilerplate.
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 simple one-parameter aggregation tool, the description names all expected results and key data-handling behaviors. Even without an output schema, an agent knows what metrics to expect. Minor ambiguity about the exact meaning of 'counted' and lack of error behavior are acceptable given the low complexity.
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 provides no description for the single 'column' parameter beyond string constraints. The description adds that the value must be a numeric column of the Intakez dataset and that numeric formats are normalized. It does not explain how to discover valid column names or whether matching is case-sensitive, but for one parameter this is acceptable.
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 lists six concrete summary metrics (count, min, max, mean, median, sum) tied to a numeric column of the Intakez dataset, making the tool's purpose explicit. It is clearly distinguishable from siblings like dataset_top or dataset_row because it returns aggregate statistics rather than rows or top values.
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 does not state when to prefer dataset_stats over sibling tools such as dataset_top, dataset_search, or dataset_columns. Usage context must be inferred from the phrase 'numeric column', but no explicit alternatives or exclusions are provided.
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 aspect of the Intakez dataset: schema, provenance, exact lookup, substring search, aggregation, ordering, and multi-value comparison. The boundaries are clear and the descriptions specify when to use each.
All seven tools share the dataset_ prefix and use concise lowercase suffixes, creating a predictable pattern. Although some suffixes are nouns and some verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped set for dataset exploration: schema, metadata, row retrieval, search, stats, top values, and comparisons are all covered without redundancy.
The surface covers the full read-only exploration lifecycle for a dataset: understand schema and provenance, filter rows, aggregate numeric columns, and rank values. No obvious dead ends for typical dataset questions.