dataset_statscount, min, max, mean, median and sum of a numeric column of the DoorsetBook 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 behavioral burden. It discloses meaningful data-handling behavior: grouping commas and currency are parsed, and non-numeric rows are excluded from calculations while still being counted. This is valuable beyond the schema, though the exact output shape is left to inference.
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 entire description is one dense, front-loaded sentence. Every clause adds information: the statistics computed, the dataset scope, input formatting, and invalid-row behavior. No filler.
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 read-only statistics tool, this is nearly complete: the return values are enumerated, and edge-case handling is described. A small ambiguity remains in how the non-numeric count is surfaced, and whether an empty numeric subset returns zeros or null is not addressed.
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%, so the description must define the lone column parameter. It does so by restricting it to a numeric column and explaining formatting handling and non-numeric treatment, which materially clarifies how the parameter is interpreted.
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 clearly identifies the operation (computing count, min, max, mean, median, sum) and the target resource (a numeric column of the DoorsetBook dataset). It is unambiguous, though it does not explicitly contrast itself with sibling tools such as dataset_top or dataset_search.
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 the obvious use case: obtaining univariate summary statistics for a numeric column. It provides clear context but does not state when an alternative tool should be preferred or mention any 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 has a distinct primary purpose—schema, provenance, exact lookup, multi-value comparison, search, stats, and top rows. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by column value, though dataset_compare is specifically for ordered multi-value comparisons.
All tools share the consistent dataset_ prefix and snake_case naming, making them easy to group. However, the suffix style is mixed: some are nouns (columns, row, stats), some are verbs (compare, search), and one is an adjective (top), which is a minor deviation from a fully uniform verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool covers a meaningful query need—schema discovery, provenance, exact lookup, comparison, search, statistics, and ranking—without unnecessary bloat or missing core access patterns.
The tool surface covers the main ways to interact with the DoorsetBook dataset: schema, metadata, exact and fuzzy lookup, comparisons, aggregates, and top/bottom rows. Minor gaps exist, such as no explicit pagination for large result sets or numeric range filtering, but common questions are well supported.