dataset_statscount, min, max, mean, median and sum of a numeric column of the WalkthroughDesk 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 disclosure burden. It adds valuable behavioral detail: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It does not disclose return format or error behavior for missing columns, but the edge-case handling is genuinely informative.
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 dense sentence that front-loads the computed metrics and adds only essential behavioral caveats immediately after. Every clause earns its place; no filler or restatement of the title.
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 low complexity — one parameter, no output schema, no annotations — the description covers the purpose, the dataset context, and important parsing behaviors. It omits the exact response structure, but the listed statistics effectively convey what will be returned.
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 coverage is 0% and there is a single 'column' parameter. The description compensates by specifying that the column must be numeric and belong to the WalkthroughDesk dataset, which is meaningful beyond the schema's bare type declaration. It could be richer still (e.g., exact column-name syntax, behavior for fully non-numeric columns), but it gives enough context for correct invocation.
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: computing summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. This clearly distinguishes it from siblings like dataset_row, dataset_search, and dataset_top, which address different operations.
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 the tool is relevant — when aggregate numeric summaries are needed — but does not explicitly state when not to use it or name the alternative for other cases. The sibling tool names offer context, but no explicit selection guidance is 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 has a clear role: schema, provenance, exact row lookup, substring search, compare, stats, and top/bottom rows. dataset_row and dataset_compare both filter rows, but the distinction between exact single-value lookup and ordered multi-value comparison is clear enough from the descriptions.
All tools follow a consistent dataset_<noun> pattern, making the tool family immediately recognizable and predictable. No mixed styles or vague verbs are present.
Seven tools is well-scoped for a dataset exploration server: schema, provenance, lookup, search, comparison, stats, and ranking cover the core operations without unnecessary redundancy.
The tool surface covers schema discovery, provenance, exact lookup, substring search, comparison, numeric statistics, and ordering, which covers most common dataset questions. Minor gaps exist such as no general multi-condition filtering or pagination for search results, but agents can work around these.