dataset_statscount, min, max, mean, median and sum of a numeric column of the Reputzo 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. It usefully discloses data-cleaning behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. That adds real behavioral context beyond merely 'compute stats', though it doesn't cover return format or error cases.
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 with no redundancy. The output metrics are front-loaded, and the parenthetical data-handling details are compact and valuable.
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 tool with no output schema and no annotations, the description covers the essential inputs and expected outputs. It's missing explicit return structure and failure behavior, but the listed metrics give the agent a clear picture of what to expect.
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 the key semantic that the column must be numeric and that formatted values are tolerated. However, it does not clarify whether 'column' is a name, index, or other identifier, leaving some ambiguity.
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?
Description states a specific operation: computing count, min, max, mean, median, and sum for a numeric column. It names the exact resource (the Reputzo dataset) and the outputs, making it immediately distinguishable from sibling tools like dataset_search or dataset_row without needing to open their definitions.
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 clearly implies when to use the tool (when numeric summary statistics are needed), but it never explicitly contrasts it with alternatives or states when not to use it. No exclusions or sibling routing 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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Most tools are clearly distinct (schema, provenance, stats, top), but dataset_row and dataset_compare can be confused since both filter by column values — the exact vs. multiple-values distinction is subtle, though search is clearly different with substring matching.
All tools follow the dataset_ prefix with a clear noun (columns, compare, provenance, row, search, stats, top), making the naming pattern perfectly consistent and predictable.
Seven tools is well-scoped for querying a single dataset, covering schema, content, search, comparison, statistics, ranking, and provenance without unnecessary bloat.
The surface covers all common dataset query operations (schema, lookup, filtering, search, stats, ordering, provenance), but there is no tool for aggregating by groups or listing dataset versions, which are minor gaps.