dataset_statscount, min, max, mean, median and sum of a numeric column of the Duesvo 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 full behavioral disclosure burden. It goes beyond the title by explaining that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which is valuable edge-case behavior for an agent 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?
The description is a single dense sentence with no filler. The statistic list is front-loaded, and the edge-case handling is compactly placed in a parenthetical.
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 single-parameter tool with no output schema, the description is complete: it names the input (numeric column), the output values (count, min, max, mean, median, sum), and the handling of non-numeric rows. No critical missing context prevents correct 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 0%, so the description must compensate for the single 'column' parameter. It does this meaningfully by clarifying that the column must be numeric and that formatting like commas and currency is tolerated, adding practical semantics beyond the bare string field.
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 a specific verb-resource pair (computes summary statistics for a numeric column of the Duesvo dataset) and enumerates the exact statistics returned. This clearly differentiates it from sibling tools like dataset_search, dataset_compare, or 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 description implies when to use the tool, namely when numeric summary statistics are needed for a column. However, it does not explicitly state when not to use it or name alternative tools, so usage guidance is mostly inferred rather than direct.
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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Most tools have clearly distinct purposes: schema, provenance, exact match, substring search, multi-value comparison, aggregate stats, and top/bottom ranking. The minor overlap between dataset_row, dataset_search, and dataset_compare could cause occasional misselection, but each description states its exact matching behavior.
All tools share the dataset_ prefix, which creates a strong family resemblance. However, the suffix is sometimes a noun (columns, provenance, row) and sometimes a verb (compare, search, stats, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for querying a single dataset. Each tool addresses a distinct common question type without redundancy or bloat.
The set covers schema discovery, provenance, exact lookups, fuzzy search, controlled comparison, numeric aggregates, and sorted extremes. A general arbitrary filter or grouped analysis is missing, but the provided tools handle the most likely dataset questions.